adrianosancarneiro/PA_MCP_Orchestrator_Server_V1

PA_MCP_Orchestrator_Server_V1

PA_MCP_Orchestrator_Server_V1 Project Specification and Implementation Plan

PA_MCP_Orchestrator_Server_V1 – Design & Implementation Plan Overview & Role in the Architecture

PA_MCP_Orchestrator_Server_V1 is the central intelligence hub of the modular AI assistant system. It bridges the Telegram bot interface and all other MCP microservices (Email, Webhook Gateway, etc.), orchestrating tool usage and managing multi-step workflows. The orchestrator interprets user requests or incoming events, decides on appropriate actions using an LLM-driven reasoning engine, and routes commands to the correct tool services. It also implements Human-In-The-Loop (HITL) logic: for certain actions (like sending emails or posting to social media), it can pause and ask for user confirmation before execution. By centralizing decision-making, the orchestrator enables a coherent conversation across services – for example, a Telegram message might trigger an email search via the Email server, or an incoming email might generate a Telegram alert and suggested response via the orchestrator.

Key responsibilities include:

MCP Command Interface: Provide a standardized interface (HTTP API and optional stdio) for receiving commands (e.g. tool/call, tool/list) from other services (Telegram gateway, Email server, etc.) and returning results.

Tool Selection & Routing: Interpret natural language queries and events, use semantic search and LLM reasoning to select the best tool or sequence of tools to solve the problem at hand.

Local LLM Reasoning: Leverage a local LLM (Meta Llama 3 8B Instruct, quantized) to understand context, generate plans, and produce conversational responses or tool arguments. The LLM serves as the “brain” for decision making and can also produce direct answers when a tool isn’t needed.

State & Memory Management: Maintain long-term memory of conversations and facts using a vector database (Qdrant) and track the state of ongoing problems/tasks in PostgreSQL. Each distinct task or user request is labeled with a problem_id and logged with all steps (tool calls, decisions, outcomes, etc.) for persistence and audit trail.

HITL Workflow Control: Decide when to execute actions autonomously versus when to involve the user. Tools have metadata (e.g. permission_required) that indicate if user confirmation is needed. The orchestrator enforces these guardrails by either performing the action or prompting the user for approval, and handles the user’s response accordingly.

Extensibility: Provide a modular foundation that can easily integrate additional tools and services. The orchestrator can accommodate future MCP modules (e.g. WhatsApp agent, Instagram agent, web browsing workflows) by simply adding new tool definitions and routing logic, without requiring changes to the core logic.

In summary, PA_MCP_Orchestrator_Server_V1 acts as the “air traffic controller” of the AI assistant ecosystem – it listens to incoming user messages or events, uses AI to figure out what needs to be done, delegates tasks to the appropriate specialist service (email, calendar, web, etc.), and consolidates the results back to the user. All of this happens while maintaining context (problem_id threads) and observing permission rules for safe operation.

MCP Interface – HTTP API & STDIO Support

To maximize flexibility in deployment, the orchestrator exposes its MCP-compatible interface over HTTP (for networked microservice usage) and STDIO (for direct process-to-process communication), configurable via startup flags or environment variables. This dual interface approach mirrors the other components (like the Webhook Gateway) which can forward events either by calling an HTTP endpoint or by launching a subprocess and communicating via standard I/O GitHub .

HTTP Interface: In HTTP mode, the orchestrator runs a lightweight web server (using FastAPI or similar) and defines endpoints for MCP commands. The primary endpoints are:

POST /tool/call: Execute a tool or orchestrated action. Expects a JSON payload describing the tool request.

GET /tool/list: Retrieve the list of available tools and their metadata.

(Optionally, a health-check endpoint like GET / for monitoring.)

For example, an HTTP POST /tool/call might receive a JSON like:

{ "cmd": "tool/call", "tool_name": "search_email", "args": { "query": "meeting schedule", "from": "alice@example.com" }, "user_id": "telegram_12345" }

The orchestrator will interpret this request, possibly consult the LLM if needed, and route to the Email server’s functionality. It then returns a JSON response such as:

{ "success": true, "tool_name": "search_email", "result": { "total_found": 2, "emails": [ { "id": "17c7051732e8c5b2", "snippet": "Project meeting is scheduled for..." }, { "id": "17c6f8d2340abf10", "snippet": "Re: Schedule for our meeting..." } ] }, "problem_id": 42, "tool_executed": true }

If the requested operation requires confirmation (for example, compose_email might need user approval to actually send), the orchestrator’s response will indicate that instead of executing immediately:

{ "success": true, "tool_name": "compose_email", "requires_confirmation": true, "confirmation_message": "Draft email prepared to bob@example.com with subject 'Project Update'. Send it now?", "problem_id": 43, "tool_executed": false }

Other services (like the Telegram bot or an email agent) can then present this confirmation to the user, and subsequently call the orchestrator again (perhaps with tool/call on a special confirm_action tool or simply resending the original request with a “confirm” flag) once the user approves.

Under the hood, the HTTP API will likely use FastAPI for its async support and Pydantic models for input/output validation (consistent with the design of existing services). For instance, we can define Pydantic request/response models for ToolCallRequest and ToolCallResponse to enforce structure. Here is a conceptual snippet of the FastAPI setup:

pa_mcp_orchestrator/main.py

from fastapi import FastAPI from pydantic import BaseModel from pa_mcp_orchestrator import orchestrator # core logic module

app = FastAPI(title="PA_MCP_Orchestrator_Server_V1", version="1.0.0", description="Central MCP Orchestrator")

class ToolCallRequest(BaseModel): cmd: str tool_name: str = None args: dict = {} user_id: str = None problem_id: int = None

@app.post("/tool/call") async def tool_call(req: ToolCallRequest): # Delegate to orchestrator logic response = await orchestrator.handle_tool_call(req.dict()) return response

@app.get("/tool/list") async def tool_list(): return orchestrator.get_tool_list()

This example shows an async handle_tool_call that will implement the core decision logic (described later) and return a dict/response.

STDIO Interface: In STDIO mode, the orchestrator runs as a background process without opening a network port. It reads JSON command objects from stdin and writes JSON responses to stdout. This mode is useful for tighter integration where, for example, the Email server might spawn the orchestrator as a subprocess and communicate directly, avoiding HTTP overhead. The orchestrator’s main.py would detect an environment flag (e.g. ORCHESTRATOR_MODE=STDIO) or command-line argument (--stdio) and enter a loop reading lines from sys.stdin. Each line would be parsed as JSON, passed to the same handle_tool_call logic, and the resulting response JSON written to stdout followed by a newline.

Pseudo-code for STDIO loop:

import sys, json from pa_mcp_orchestrator import orchestrator

if mode == "STDIO": for line in sys.stdin: try: request = json.loads(line.strip()) except Exception as e: print(json.dumps({ "success": False, "error": "Invalid JSON"})) continue if request.get("cmd") == "tool/call": response = orchestrator.handle_tool_call(request) print(json.dumps(response)) elif request.get("cmd") == "tool/list": tools = orchestrator.get_tool_list() print(json.dumps({ "tools": tools })) # flush output if needed, handle exit conditions...

This allows the orchestrator to behave like a REPL server. The mcp_client.py in other services (e.g. the Webhook Gateway) can choose whether to use HTTP or spawn this process based on config GitHub . For instance, the gateway might have a setting MCP_MODE="HTTP" or "STDIO" to pick how it forwards events GitHub – the orchestrator honors both.

MCP Protocol: The orchestrator speaks in the common MCP JSON protocol understood by all components. In practice, this means it expects the cmd field and associated data as shown above. It will likewise produce results in a consistent format with keys like success, tool_name, result, error, etc., so that calling services can easily consume the output. This consistency is crucial for plug-and-play compatibility among the microservices.

By supporting both HTTP and STDIO, the orchestrator can be deployed as a standalone microservice (listening on a local port for requests from the Telegram gateway, Email server, etc.) or embedded directly (for example, an all-in-one deployment might run everything in one process tree for simplicity). In either case, the orchestrator’s logic remains the same.

Project Structure and Modules

The project is organized for clarity and modularity, separating concerns into different files and submodules. A possible directory structure is:

PA_MCP_Orchestrator_Server_V1/ ├── README.md # Documentation and usage instructions (high-level summary of this design) ├── pyproject.toml # Python project metadata and dependencies ├── .env.example # Sample environment variables (DB URL, Qdrant URL, model path, tokens) ├── pa_mcp_orchestrator/ # Package containing the orchestrator code │ ├── init.py │ ├── main.py # Entrypoint: parses flags, starts HTTP server or STDIO loop │ ├── config.py # Configuration loader (DB creds, token paths, model path, etc.) │ ├── server.py # If using a separate module for server logic (could merge with main) │ ├── orchestrator.py # Core orchestration logic (tool routing, planning, execution) │ ├── tool_registry.py # Tool schema definitions and loading (e.g. from YAML/DB) │ ├── llm_client.py # Interface to local LLM (loads model, generates completions) │ ├── memory.py # Qdrant vector store integration for memory & semantic search │ ├── database.py # PostgreSQL integration (connection pool and helper methods) │ ├── models/ # (Optional) Pydantic models or dataclasses for requests/responses and DB records │ └── utils.py # Utility functions (logging setup, common helpers) └── deploy/ ├── orchestrator.service # Example systemd unit file for running orchestrator (optional) └── Dockerfile # (Optional) Docker setup for containerized deployment

Brief module descriptions:

main.py: Application entrypoint. It reads configuration (possibly via config.py or environment variables) and determines run mode (HTTP or STDIO). In HTTP mode, it starts the FastAPI/Uvicorn server (or Flask app) and includes the endpoint route definitions if not split out. In STDIO mode, it invokes the loop to listen on stdin. It may also perform one-time initialization such as loading the local LLM model into memory and warming up the vector database connections at startup.

config.py: Centralized configuration management. This module loads environment variables or secrets.txt paths for credentials. For example, it will load the PostgreSQL connection URL or components (host, user, password, db name), the Qdrant host (192.168.0.83) and port, the LLM model file path (e.g. a path to Meta-Llama-3-8B-Instruct.Q5_K_M.gguf on disk), and any token or credential file paths (like OAuth tokens for Gmail, OpenAI API key, etc. if needed). By isolating config here, the rest of the code can just import config and use config.DB_URL, config.QDRANT_HOST, etc., rather than scattering environment lookups around.

server.py: Implements the interface layer. If using FastAPI, it would instantiate the FastAPI app and define the @app.post("/tool/call") and @app.get("/tool/list") endpoints (as shown in the snippet above). It might also handle JSON serialization/deserialization for STDIO mode if not done in main. Essentially, this module translates incoming requests (HTTP or STDIO) into calls to the core orchestrator logic and formats the responses back to the client.

orchestrator.py: The heart of the system. This module contains the orchestrator’s decision-making and routing algorithms. It exposes functions like handle_tool_call(request_dict) and get_tool_list(). Inside handle_tool_call, the logic will:

Parse the request (which tool or message is being handled, and any provided args or context).

If the request is a direct tool invocation (tool name explicitly given), possibly verify the tool exists and then either execute it or delegate to the appropriate service.

If the request is a free-form user message (e.g., coming from Telegram with no specific tool specified), invoke the Tool Routing/Planning process (see next section) to determine the best course of action (which might be a direct answer via LLM or one or more tool calls).

Coordinate the execution of the chosen tool(s) – possibly by calling another service’s HTTP API or via an SDK – and gather the results.

Apply HITL logic: if a tool requires user permission (permission_required), do not execute it immediately. Instead, generate a confirmation prompt (with help from the LLM if needed) and mark the problem as awaiting confirmation. If the tool is safe to run autonomously, execute it and get results.

Log the action and update problem state in the database (each step logged with problem_id, tool name, action, etc.).

Format the final response dict to return to the caller (including any results or messages).

The orchestrator may also contain sub-routines for multi-step planning – for example, if a user’s request is complex, it could break it into sub-tasks and plan a sequence of tool calls. To keep things modular, some of this planning logic might be split into a planner submodule or just neatly organized within orchestrator.py (perhaps using helper classes for different workflow types).

tool_registry.py: Manages the definitions and metadata of available tools. It will load tool information either from a YAML file (similar to agent_tools.yaml in the previous system) or from the database (the agent_tools table) on startup. Each tool definition includes fields like tool_name, a human-readable description, parameter_definitions (what inputs it accepts), an action_permission level (e.g. autonomous, autonomous_with_guardrails, or permission_required GitHub ), tool_type/category (email, web, etc.), and possibly tags or example usage. The registry provides:

get_all_tools() to list tools (for tool/list command).

find_tools_by_name() or similar for exact matching.

Semantic search interface: It integrates with the memory.py (Qdrant client) to perform vector similarity search over tool embeddings. On initialization, it will ensure the Qdrant collection (e.g. "agent_tools") exists and is populated with embeddings for each tool GitHub GitHub . If Qdrant is not available, it can fall back to a simpler keyword search or a static list (ensuring the system still functions in a degraded mode GitHub ).

Perhaps caching of results or an in-memory index for quick access.

This module effectively provides the orchestrator with “what tools do I have and what can they do?” and “given a query, which tools might help?”.

llm_client.py: Interface to the Local LLM. This module will load the Meta Llama 3 8B Instruct model (quantized .gguf file) using an appropriate library (for example, llama-cpp-python or a similar GGUF-compatible inference library). The model should be loaded once at startup and kept in memory for reuse. llm_client.py provides methods like:

generate_completion(prompt: str) -> str: to get a raw completion from the model.

chat(system_prompt, conversation_history) -> str: to generate a response in a conversational setting.

Higher-level methods for orchestrator’s needs, e.g. select_tool(user_query, tool_options) -> str or plan_actions(user_query, context) -> ActionPlan. These might implement the prompting logic where the LLM is given the user’s request and descriptions of top relevant tools and asked to decide which action to take (or whether it can answer directly).

Model usage: Since the model is only 8B and quantized, it should be feasible to run on a local GPU or CPU. The llm_client can be configured for performance (e.g., number of threads, using GPU layers if available). The orchestrator will use the LLM in a few places:

Understanding user requests: e.g., given a message “I need to schedule a meeting with Bob next week and also find last quarter’s report,” the LLM can parse that into two intents: one needing a calendar tool (schedule) and one needing an email/file search tool.

Tool selection reasoning: The LLM can be prompted with the user query plus a list of tool descriptions to pick the best tool or sequence. For example: “User wants to schedule a meeting… Tools available: (1) schedule_reminder – sets a reminder; (2) compose_email – drafts an email; (3) calendar_add_event – adds an event to calendar; etc. The best tool is calendar_add_event because…”.

Direct answer generation: If the query is something answerable by the LLM’s own knowledge or reasoning (e.g. “Calculate 15% of 85” or a general question), the orchestrator can let the LLM respond directly without any tool.

Result summarization and response phrasing: After a tool executes, the LLM might be used to format the output nicely or to compose a message to the user. For instance, if a tool returns raw data (like a SQL query result or a weather API JSON), the LLM can turn that into a friendly sentence or table.

The LLM client will not call external APIs for AI (unless configured to use OpenAI as a fallback) – by default it uses the local model offline, in line with not redoing OAuth or external calls. However, the design can include an optional OpenAI API usage: if OPENAI_API_KEY is present (loaded via secrets.txt or env), the orchestrator could use an OpenAI model for certain tasks (like if a higher quality answer is needed or for embedding generation with text-embedding-ada-002 instead of local BGE). This would reuse the existing token path from the main project, avoiding a new OAuth flow for OpenAI.

memory.py: Integration with Qdrant vector store for long-term memory and semantic search. This module uses the Qdrant client (via qdrant-client Python SDK) to connect to the instance at http://192.168.0.83:6333. It likely manages two collections:

agent_tools (or similar) for tool embeddings (as mentioned above via tool_registry). Each tool’s description is embedded (using a sentence-transformer model like BGE or possibly the LLM itself to generate embeddings) and stored, so we can quickly retrieve relevant tools for a query GitHub .

long_term_memory for conversational or knowledge memory. This could store important facts or summaries from past interactions. For example, after a lengthy support conversation, a summary vector could be saved so that weeks later the system can recall context. Or if the user has certain preferences (extracted by the LLM and saved as facts), these can be vectorized for later retrieval. Each memory entry might include metadata like user_id or problem_id so we can filter by user or context when searching.

The memory.py module provides functions to add a memory entry (store a vector with some reference text/metadata) and to query memories given a new input (returning similar past items). This is useful for context retrieval (RAG – Retrieve Augment Generate): if the user references something from earlier or asks a question that the assistant has seen before, the orchestrator can fetch those related bits and supply them to the LLM for better answers.

Additionally, this module assists the tool routing by executing similarity searches for tool suggestions. For example, orchestrator might call memory.find_similar_tools(query_embedding, top_k=3) which uses Qdrant to get the best matching tool vectors. It can also apply filters – e.g., don’t return tools that require permission if we’re in fully autonomous mode, or filter by tool category if the context indicates (this capability exists in the design to use metadata conditions in Qdrant queries GitHub ).

database.py: Handles PostgreSQL connectivity and schema interactions. This module likely uses an async driver like asyncpg or sync driver like psycopg2 (depending on whether we stick to async end-to-end or allow sync DB operations in threads). It will provide:

A method to get a DB connection or session (get_conn()), possibly reading the DSN from config.py. This could manage a connection pool for efficiency.

High-level functions or classes corresponding to the Problems and ProblemSteps tables. We will implement the schema to track problem-solving state:

problems table: Fields might include id (PK), user_id (to associate with a user or conversation), title or short description of the problem, status (e.g. “in_progress”, “awaiting_user”, “done”, “blocked”), problem_type (optional categorization, e.g. “email_composition”, “general”, “issue_detection”), context (JSON field for any structured context like email draft data), definition_of_done (optional text describing what completion means), timestamps, etc.

problem_steps table: Fields include id (PK), problem_id (FK to problems), step_number (order of events in that problem), actor (“user” or “agent/orchestrator”), action (e.g. “tool_selected”, “tool_executed”, “user_confirmation”, “finished”), description (human-readable log, like “User asked to send an email” or “Orchestrator suggested tool X”), detail (JSON field for structured details, e.g. the exact tool args or the result snippet), and timestamp.

Possibly an agent_tools table if not already created, to store tool definitions along with an embedding_vector_id to link with Qdrant vector (the previous system had such schema GitHub ).

The ProblemTracker class (similar to what existed in PA_V2) can be reimplemented here to simplify interacting with these tables GitHub GitHub . For example, ProblemTracker.start_problem(title) inserts a new problem row and returns a new problem_id GitHub , log_step(actor, action, description, detail) inserts a new step into problem_steps GitHub , update_status(status) sets the problem’s status GitHub , etc. By using this helper, the orchestrator logic can record what it’s doing with one line calls, making the code cleaner.

Logging every step to the DB ensures we have an audit trail and can resume or inspect tasks. For example, if the system crashes or restarts, it could look up any problems that were in status='awaiting_user' (meaning we asked the user something and haven’t gotten a reply yet) and restore that context. It also allows the /tool/list or other debug commands to show what’s currently in progress (e.g., an admin command to list all open problems).

utils.py: Miscellaneous utilities. This might include:

A standardized logging setup (to log to console or file with consistent format, including the problem_id in log messages for traceability).

Helper to load the secrets.txt if needed (though likely we use env vars directly).

Utility for text cleaning or formatting (e.g., truncating text to fit into prompts or preparing markdown formatting for Telegram).

All code is written with modularity and maintainability in mind: each component has a single clear purpose, and complex logic (LLM prompting, DB operations, etc.) is encapsulated in functions or classes, making it easier to test and update.

Tool Orchestration & Decision Logic

One of the orchestrator’s core functions is to intelligently route requests to the correct tools, possibly chaining multiple tool calls, with the guidance of the LLM. This involves several steps:

Tool Registry & Semantic Retrieval

When a free-form query or task comes in (for example, from a Telegram message), the orchestrator doesn’t immediately know which tool to use. It leverages the tool registry and vector search to narrow the options:

Semantic Search: The user’s request (or problem description) is embedded into a vector (using the same embedding model used for tools, e.g. BGE 768-dimensional GitHub if available, or the LLM itself in embedding mode). The orchestrator queries the Qdrant collection of tools with this vector to find the top N most similar tool descriptions GitHub . For instance, if the user says "I want to schedule a meeting next week," this should surface tools related to calendar or reminder. If the user says "What’s the weather like?" it should bring up the get_weather tool. This semantic approach allows understanding of phrasing beyond simple keywords (improving accuracy over naive keyword matching).

Metadata Filtering: The orchestrator can apply filters. For example, if a user is not an admin, perhaps certain tools are off-limits (this can be enforced by permission level). Or if the query context suggests the user is currently composing an email (problem_type = email_composition), the orchestrator might limit the tool suggestions to email-related actions.

Result: Candidate Tools: After this step, we have a short list of candidate tools, each with a similarity score and metadata like action_permission GitHub . If the top tool has a very high similarity and a safe permission level, the orchestrator might choose it directly. Otherwise, it will proceed to reasoning with the LLM to confirm the best action.

LLM Reasoning & Planning

The local LLM is utilized to make the final decision on which tool to invoke and how. Depending on the scenario, the orchestrator might use one of several prompting strategies:

Single-Step Tool Selection Prompt: Provide the LLM with the user’s request plus a brief summary of the top candidate tools (name + description), and ask it to pick the most appropriate action. For example:

System prompt: “You are an AI Orchestrator with access to various tools. The user’s request is: ‘{user query}’. The following tools are available:

{Tool1 name}: {Tool1 description}

{Tool2 name}: {Tool2 description}

{Tool3 name}: {Tool3 description}

Decide which tool (if any) is best to help the user. Answer in JSON with either a direct answer or a tool invocation.”

The LLM might respond with a JSON like: {"action": "tool", "tool_name": "schedule_reminder", "tool_args": {"date": "next Tuesday 10am", "description": "Meeting with Bob"}} or {"action": "answer", "response": "It's going to rain tomorrow with a high of 18°C."}. The orchestrator would parse this JSON (ensuring it’s well-formed, possibly by using a JSON-enforcing decoding as in some LLMClient implementations).

Multi-Step Planning (Chain of Thought): For more complex tasks or when an initial tool’s result is needed to continue, the orchestrator can engage the LLM in a loop:

LLM proposes an action (tool or answer) along with reasoning.

Orchestrator executes the tool if one is proposed (and if autonomous).

Feed the result back into the LLM, allowing it to decide next step.

Continue until the LLM outputs that the task is complete or requires user input.

This resembles the ReAct pattern (where the LLM reasons and acts in turns). An example: User: “Find any upcoming events for this week and email me a summary.” The LLM might reason: first call a calendar_check tool to get events, then call compose_email with that info. The orchestrator will carry out each step in sequence. This kind of tool chaining is supported by maintaining the problem_id context and letting the LLM refine the plan with intermediate data.

Direct Answer Mode: If the best course of action is just to answer the user (e.g., they asked a question that doesn’t require an external tool or data lookup), the orchestrator can let the LLM generate a direct answer. In this case, the orchestrator sets is_direct_answer=True in the response and passes the LLM’s message back to the user.

The orchestrator’s logic will interpret the LLM’s output. If a tool is chosen, it will cross-check:

Is this tool allowed to run automatically? (Check action_permission in the registry.)

If permission_required, the orchestrator will not execute it immediately. Instead, it will prepare a confirmation. Possibly it uses a template or the LLM to generate a friendly confirmation message, e.g., “🔧 The assistant wants to send an email to Bob with subject ‘Project Update’. Do you approve?”. It then returns a response indicating requires_confirmation: true as shown earlier, and records in the DB that this problem is waiting for user input. Execution will pause here.

If autonomous or autonomous_with_guardrails, the orchestrator proceeds to execute the tool. (For autonomous_with_guardrails, you might implement additional checks or a sandboxed execution depending on the tool type. For example, a run_sql tool might be autonomous but with guardrails: the orchestrator could automatically run a read-only query but require confirmation if the SQL tries to modify data GitHub .)

Tool Execution & Integration

Executing a tool might mean calling another MCP microservice or performing an internal operation:

For Email-related tools (search_email, get_email_details, reply_email, compose_email), the orchestrator will delegate to PA_MCP_Email_Server_V1. If that service exposes an HTTP API, the orchestrator will send a request (e.g., POST http://127.0.0.1:8002/email/search with appropriate parameters) and await the response. Alternatively, if configured to use STDIO mode, it might launch/communicate with the email server’s process similarly to how the webhook gateway does. The orchestrator uses the credentials from secrets.txt indirectly here – i.e., the Email server already has Gmail OAuth tokens loaded, so the orchestrator doesn’t need to re-authenticate Gmail, it just requests the email server to act. This fulfills the “reuse Gmail tokens, avoid new OAuth flow” requirement: Gmail integration is handled in the dedicated server with existing tokens GitHub .

For tools like “web_search” or “get_weather”, the orchestrator might call external APIs. For example, web_search could call a search API or use a local index. get_weather might call an open weather API. These can be implemented either as part of orchestrator (since they are relatively straightforward API calls) or as separate microservices (for consistency). In a first version, we can implement them inside orchestrator as internal tools: e.g., orchestrator has a function _tool_get_weather(location) that fetches data from a weather API (with an API key from secrets.txt). This keeps the orchestrator self-contained for some simple utilities.

For database query tool (run_sql) or other local utilities, orchestrator can execute them directly if it has access (for run_sql, likely it connects to a specific database and runs the query with read-only permissions, returning results). Guardrails (like preventing destructive queries) should be applied – e.g., parse the SQL or enforce it’s SELECT-only, since this was noted as a feature GitHub .

For future social media tools (WhatsApp, LinkedIn, etc.), the orchestrator would similarly call out to those specialized agents. E.g., if a send_whatsapp tool is defined, orchestrator might call PA_MCP_WhatsApp_Server_V1 via HTTP with the message to send. Designing the orchestrator this way means adding a new tool is as simple as defining it in YAML/DB and coding the integration to the new service – the LLM can then automatically consider it in its toolkit (especially if we update the tool embeddings).

The orchestrator should design the tool execution layer to be easily extendable. A pattern to use is a mapping of tool_name to an executor function or endpoint. For example, in code:

TOOL_EXECUTORS = { "search_email": lambda args: email_client.search_emails(**args), "compose_email": lambda args: email_client.compose_email(**args), "get_weather": lambda args: weather_api.get_weather(**args), "run_sql": lambda args: database.run_query(**args), # etc... }

Where email_client might be an object in pa_mcp_orchestrator that wraps calls to the email server, and weather_api could be a small internal module for fetching weather. The orchestrator can then do:

if tool_name in TOOL_EXECUTORS: result = await TOOL_EXECUTORS else: result = {"error": f"Unknown tool {tool_name}"}

This structure isolates tool-specific code from the main logic and makes adding new tools straightforward (just register a new executor function).

Logging and Problem Tracking

Throughout the above process, the orchestrator logs significant events both to console/file (for debugging) and to the PostgreSQL database for persistent tracking:

When a new user request or event is received that is not part of an existing active problem, the orchestrator creates a new entry in the problems table via ProblemTracker.start_problem, obtaining a problem_id GitHub . It may set the title to a short summary (e.g., “Schedule meeting and find report” from the earlier example) and status to “in_progress”. The user_id (like a Telegram user ID or email account ID) is stored so we know who this problem is for.

Each decision or action is recorded with ProblemTracker.log_step. For instance:

actor='user', action='request', description = the raw user message (or event) starting the problem.

actor='agent', action='tool_suggested', description = “Suggested tool X via LLM” (with a detail containing the candidate tools and scores perhaps).

actor='agent', action='tool_executed', description = “Executed tool X”, detail containing the args passed and a short result summary.

If waiting for confirmation: actor='agent', action='await_confirmation', description = “Awaiting user confirmation to execute tool Y”.

If user responds “yes” or “no” via Telegram: actor='user', action='decision', description = "User confirmed operation" (or "User canceled operation") GitHub .

If error occurs: actor='agent', action='error', description = error message.

The ProblemTracker.update_status is used to mark completion (status='done' when finished) or blockage (status='blocked' or waiting if stuck). For example, once an email is successfully sent or the answer is given to the user, the orchestrator marks the problem done GitHub . If it’s awaiting user input, status might be set to “pending_user”.

Using these logs, we can implement commands like a Telegram /tasks command that asks orchestrator (via tool/list or a dedicated endpoint) to list active problems for that user. The orchestrator can query problems and return any that are not done, possibly with a summary of what’s pending (as was done in the previous system’s Telegram bot using ProblemTracker.get_user_active_problems() GitHub GitHub ).

Logging to the console/file will be done with meaningful messages (including problem_id for correlation). For instance:

INFO [Problem 42] Received user request: "Find my recent emails from Alice" INFO [Problem 42] LLM chose tool: search_email (permission=autonomous) INFO [Problem 42] Executing tool search_email with args={'query': 'Alice', 'days_back': 7} INFO [Problem 42] Tool execution completed: 3 results found INFO [Problem 42] Responded to user with email search results, problem marked done.

This gives a live trace when debugging and also helps in unit tests to verify the flow.

HITL (Human-in-the-Loop) Workflow

The orchestrator’s design ensures that if a tool needs user approval or input, the workflow cleanly pauses and resumes:

When a requires_confirmation situation arises, the orchestrator sends the confirmation prompt to the user (via the calling interface, e.g., the Telegram bot will simply relay the orchestrator’s response message which includes the prompt). It also stores the pending action somewhere so it knows what to do if the user confirms. There are a couple of ways to do this:

Simply include all needed info in the confirmation_message and in the DB. For example, orchestrator could stash the pending tool_name and args in the problem_steps.detail or in a dedicated field in problems (like a JSON context or a separate pending_actions table). In the previous system, they might have kept it in the conversation state; here we can rely on the DB for persistence.

Alternatively, the orchestrator might generate a one-time token or reference ID for the action and send that in the confirmation (like confirm_id). But probably overkill if we have the problem_id and we know the last step is awaiting confirmation.

When the user replies with “yes” or “no” (or clicks a button in Telegram), the Telegram gateway (or whichever interface) will call the orchestrator again, likely with something like tool/call for a special meta-tool or simply a structured message. One approach is to have a pseudo-tool called "confirm_action" with parameters problem_id and decision (true/false). So the gateway could call: {"cmd": "tool/call", "tool_name": "confirm_action", "args": {"problem_id": 43, "confirmed": true} }. The orchestrator’s handle_tool_call sees this, and looks up problem 43 in the DB:

If confirmed, it retrieves the stored pending tool and args, logs the user confirmation, and then proceeds to execute the tool for real this time. After execution, it would log the result, update status, and return a message like “✅ Email sent successfully.”

If denied, it logs that the user canceled, updates status (perhaps to “canceled” or just closes the problem), and returns a message like “❌ Action canceled. Let me know if you need anything else.” No further action is taken.

This approach cleanly separates the confirmation logic. It’s essentially what was happening in the Telegram bot code, but now centralized. For instance, previously they set context.user_data['awaiting_confirmation'] and handled it in the next message GitHub . In the new design, the state is at orchestrator side, which is better since it can work across interfaces (imagine confirming via a different interface – e.g., maybe the user gets an email asking for confirmation – the orchestrator could handle that too as long as it receives a confirmation command).

Besides confirmations, other HITL interactions include cases like the conversational email composition workflow. As seen in PA_V2, composing an email was done through iterative prompts (first ask for recipient, then subject, then body, then confirm sending) GitHub GitHub GitHub . In the new orchestrator, this can be managed by:

Using the problem’s context field to store the draft email content (to, subject, body as it’s built up).

After each user message in that context, the orchestrator checks if required fields are filled. If not, it asks the next question. If all info is gathered, it might even use the LLM to polish the email text or just proceed to confirm sending.

This essentially replicates ConversationalEmailComposer logic GitHub GitHub within the orchestrator. We might not hardcode it, but let the LLM handle more of it. Alternatively, treat it as a specialized multi-turn tool: i.e., compose_email when called without all fields triggers a workflow of its own. For now, a simpler path is to leverage the existing approach: orchestrator can detect phrases like “compose email” and if the user hasn’t provided all parts, orchestrator can ask for missing pieces one by one, using problem_id to remember what’s done. This is one area where a rule-based approach can complement the LLM to ensure deterministic gathering of required info.

In all HITL cases, the orchestrator ensures the conversation stays on track by checking if there’s an active problem awaiting input whenever a new message comes from the user:

If yes, it knows to route that message to that problem’s handler (e.g., treat it as the next step in composing an email or as answering a yes/no confirmation).

If no, then it treats the message as a new query and starts fresh.

Example Usage Scenarios

To illustrate how PA_MCP_Orchestrator_Server_V1 operates in context, consider a couple of scenarios:

1. Telegram User Request -> Orchestrator -> Email Tool

User (via Telegram): "Hey, can you find any emails from Alice about the Q3 report?"

The Telegram bot (which could be the Webhook Gateway component) receives this message and forwards it to the orchestrator via tool/call with perhaps {"user_id": "<telegram_user_id>", "text": "...user message..."}. If using HTTP, this might hit an endpoint like /tool/call with a JSON containing the raw text (the absence of a tool_name signals orchestrator to figure out the tool).

Orchestrator: Creates a new problem entry (say problem_id=101) for this request. It uses the tool registry’s semantic search: the query “emails from Alice Q3 report” will likely surface search_email and maybe get_email_details as relevant tools. The orchestrator asks the LLM: the LLM sees this is clearly an email search query. It responds with an action to use search_email with args {"query": "Q3 report", "from_email": "alice@example.com"}.

Orchestrator checks search_email tool metadata: it’s presumably autonomous (safe read-only action). So it logs the plan (tool selected) and calls the Email server’s API, e.g., GET /email/search?query=Q3%20report&from=alice@example.com&user_id=<gmail_account>. The Email server (which has Gmail API access) performs the search and returns a JSON result with matching emails.

Orchestrator receives the results, logs the tool execution and outcome (maybe 2 emails found), and then formats a response. It could either let the LLM summarize the results or use a built-in formatter. For a search, perhaps it returns a short list of subjects and dates.

The orchestrator responds back to the Telegram service (HTTP response or via stdout) with a nicely formatted message containing the search results (or perhaps just the raw JSON which the Telegram bot then formats into a message). In our earlier example, the response JSON had the found emails.

The Telegram bot sends the user a message: “📧 Emails from Alice about 'Q3 report':\n1. [Subject: Q3 Report Summary] – Oct 5, 2025\n2. [Subject: Re: Q3 Report Draft] – Oct 3, 2025”.

The problem 101 is left open if the user might ask to act on those emails, or it could be marked done since the query was answered. This is a design choice – simple Q&A could be one-and-done. Let’s assume done for now. The DB now has a complete log of this transaction for future reference.

2. Incoming Email Event -> Orchestrator Suggests Action

Scenario: The Gmail account receives a new email from a system monitoring service with subject “❗Server CPU High Alert”.

The Gmail Webhook Gateway (PA_Webhook_Gateway_V1) gets the Pub/Sub notification and filters it. It sees a new important email. It forwards the event to the MCP Email Server (PA_MCP_Email_Server_V1) via HTTP (e.g., POST /sync_email) with details of the message ID and account GitHub .

The MCP Email Server fetches the full email content via Gmail API. Suppose the email body says: “The server CPU has been above 90% for 5 minutes. Please investigate. – Monitoring System”. The Email server can then invoke the orchestrator, because this looks like a problem that the AI assistant might help with (it’s not just a normal user email to display).

The email server calls the orchestrator’s MCP interface, maybe like: tool/call with tool_name: "incoming_email" or simply treat the email content as a user message to analyze. It could provide a structured payload such as:

{ "cmd": "tool/call", "tool_name": "analyze_email", "args": { "from": "monitor@company.com", "subject": "Server CPU High Alert", "body": "The server CPU has been above 90% for 5 minutes. Please investigate." }, "user_id": "system" }

(Where analyze_email is a pseudo-tool that instructs the orchestrator to analyze an email event.)

Orchestrator: Receives this, creates a new problem (e.g., id=102, title “Server CPU High Alert”). It might use LLM to classify the email and determine possible responses. Perhaps we have a tool like run_diagnostic or create_incident_ticket in the tool list for such cases. If such tools exist, the process is similar: semantic match, LLM reasoning. If not, the orchestrator may still notify the user.

Let’s say no automated tool exists for server issues in this version. The orchestrator decides this is an “emerging problem” that the user should be alerted about and suggests a course of action. It could formulate a message to the user’s Telegram: “⚠️ Alert: Received a Server CPU High alert. I recommend checking the server’s processes. Shall I run a diagnostic tool or send an email to IT?”

How to get this to the user? The orchestrator might know which user(s) should handle it (perhaps link the system user_id to a real user’s Telegram via config). If the system is just for the one primary user, it can directly send a Telegram message. Since orchestrator is not directly connected to Telegram (the gateway is), it could achieve this by returning a special response that the Email server or gateway relays. For example, orchestrator’s response might be:

{ "success": true, "tool_name": "notify_user", "result": { "message": "⚠️ Alert: Server CPU usage is high. Recommend investigating. [Options: run diagnostic?]" }, "problem_id": 102 }

If the Email server receives this, it could forward it to Telegram (this part of integration design can vary – perhaps the orchestrator should have a more direct way to notify, but keeping it consistent, we use the same path).

The user gets the alert on Telegram via the orchestrator->gateway pipeline. The user can then respond with a command or confirmation, e.g., “Yes, run diagnostic.” That comes back to orchestrator as a message. Or maybe the orchestrator offered buttons: “/run_diagnostic” or “/ignore”. If the user says run diagnostic:

Orchestrator receives that (as a new tool/call request or just a message in problem 102 context). It then executes the run_diagnostic tool (if it exists, or maybe it was an imagined suggestion – in a real case, could be a tool that SSHes into a server or queries a monitoring API).

It then sends the results back to the user, and possibly marks problem 102 done or in progress awaiting further steps.

This scenario shows how the orchestrator can detect a problem from an incoming email and initiate a proactive workflow. Even if no automated fix is available, it adds value by notifying the user and offering to help (like a smart filter that turns emails into actionable tasks).

3. Multi-turn Conversation Example

User: "I need to book a flight to NYC next week and also let John know I'll be there."

This single message contains two tasks: booking a flight (which might involve a travel tool or at least a step-by-step with the user) and emailing John. The orchestrator would:

Create a problem (id 103).

Use LLM to parse the request. The LLM might not have an actual flight-booking tool, but it knows one part is travel (which maybe no tool exists for yet) and the other part is emailing John. It might suggest using compose_email to John about the NYC trip, and for the flight, maybe it can’t complete automatically.

The orchestrator might respond: “✈️ I can help email John about the trip. (No flight booking tool is available, but I can remind you to book one.) Draft email to John now?” – essentially it handles what it can (the email) and acknowledges the other part. Alternatively, if we had a web_search tool, it could search flights or open a browser, but let’s assume not.

The user says “Yes, draft the email.” Now orchestrator goes into the email composition sub-workflow for John: it will likely ask “Sure. What should the email say, or should I summarize your trip details?” and so forth, collecting details and then sending the email. Each message back and forth stays under problem 103, logged and contextual, so the orchestrator knows we’re in the middle of a task.

Once done (email sent), it might mark that part done and offer: “📅 I’ve emailed John. I’ll set a reminder for you to book the flight tomorrow.” (if we decide to use the schedule_reminder tool for the unfinished task). It could then call schedule_reminder internally to remind the user the next day via Telegram about flight booking.

This ends up being a multi-tool, multi-turn orchestration, demonstrating the orchestrator’s ability to manage a toolchain and carry context (John’s name, trip info, etc.) across steps with the LLM’s help.

Integration with Other MCP Services

The orchestrator is designed to work hand-in-hand with other services in the Personal Assistant ecosystem:

Telegram Bot / Webhook Gateway: The Telegram interface (either via a polling bot or the webhook gateway) acts as the UI to the user. It does minimal work: essentially just converting Telegram updates to MCP commands and sending them to the orchestrator, then taking the orchestrator’s responses and sending messages or interactive prompts back to Telegram. For example, the gateway might receive a message and do:

Pseudo-code in Telegram gateway context

orchestrator_response = orchestrator_client.call_tool({ "cmd": "tool/call", "user_id": telegram_user_id, "text": incoming_text })

Then handle the orchestrator_response to craft Telegram reply.

The orchestrator can handle all logic of what to do, the gateway just handles how to display or forward it. This separation means we can change the brain (orchestrator) logic without redeploying the Telegram bot code – it just pipes data through.

If the Telegram bot supports inline buttons (like for confirmations), the orchestrator can include in its response some indication for button labels. For instance, in a confirmation scenario, orchestrator could include something like:

{ "requires_confirmation": true, "confirmation_message": "Send this email?", "confirm_options": ["Yes", "No"] }

The Telegram gateway could map that to actual button UI. When clicked, it sends back the appropriate command (maybe “Yes” triggers a confirm_action call as described earlier). This was partly implemented in PA_V2 (with InlineKeyboard and callback queries) and can be continued here but through the orchestrator’s standardized protocol rather than Telegram-specific code in the logic layer.

Email Server (PA_MCP_Email_Server_V1): This service encapsulates Gmail/Outlook integration – retrieving emails, sending emails, syncing contacts, etc. The orchestrator relies on it for any actual email data. The communication is likely via HTTP calls:

Orchestrator calling Email API: e.g. email_server.list_threads(query) or email_server.send_email(draft). The email server, in turn, uses stored OAuth tokens to interact with Gmail. The orchestrator does not handle OAuth or Gmail API directly, preventing duplicate setup. The secrets.txt might contain the paths for GMAIL_TOKEN_PATH and client secrets; those would be used by the email server (as configured in the gateway’s .env GitHub ). The orchestrator might only need the Gmail address or account ID to tell the email server which account to use (if multiple).

Email server calling Orchestrator: In scenarios like the alert example, the email server may ask orchestrator for help analyzing or deciding on an email. Another case: the email server might detect an email that looks like a meeting request or a task assignment from someone. It could then call orchestrator to handle it (maybe using an NLP classifier or simply certain rules like subject contains “Please do”).

The interface between these servers should again use the MCP format for consistency. Perhaps the email server has internally a small MCP client similar to the gateway’s, to forward things to orchestrator. For instance, if an email arrives that needs an AI summary, the email server could call:

{ "cmd": "tool/call", "tool_name": "summarize_email", "args": { "email_id": "", "user_id": "<telegram_user_id>" } }

Then orchestrator would retrieve the full email content (maybe by calling back the email server’s API to get details) and then use LLM to summarize it, and finally send the summary to the user.

Database and Knowledge Systems: The orchestrator is connected to the same PostgreSQL database that other services can use. If needed, other services might query the problems or problem_steps for information (though generally orchestrator itself provides any needed info via API to others). For example, the email server could log some info in the problem_steps when it completes an email send. However, a cleaner design is to have orchestrator log those steps on behalf of the email server when it gets the response. We should avoid multiple writers to the same log to keep it centralized.

Future Services: Integrating a new service (say WhatsApp messaging) would involve:

Creating a microservice for WhatsApp that handles that platform’s webhooks or API and likely has its own tokens (like Facebook Graph API token).

Defining new tools like send_whatsapp or check_whatsapp_messages.

Adding those tool definitions to the orchestrator’s registry (with appropriate descriptions and permission levels).

The orchestrator can then orchestrate interactions involving WhatsApp. For example, if the user says “send a WhatsApp to Alice: I’m running late”, the orchestrator (with an updated tool list) could identify a send_whatsapp tool and call that service, just as it would for an email. The flexible design means the LLM doesn’t care whether a tool is for email or WhatsApp – it just sees a description and decides if it fits the request.

Similarly, for web-based workflows, e.g., a “search the web” or “fill a form” tool: orchestrator can guide the LLM to use those tools if defined.

Human Override: Because orchestrator logs everything and uses a standard protocol, a human operator (like the user themselves or a developer) could manually intervene by calling orchestrator commands. For instance, from a CLI or an admin UI, one could query the status of a problem or force-execute a step. This is facilitated by the clear API and the database record.

In all integrations, security and authorization must be considered. The orchestrator should ensure that only authorized requests from trusted services are accepted – especially if running an HTTP server. In a local deployment (everything on the same machine or secured network), this might be as simple as binding to localhost or requiring an API key for HTTP calls. Since the Webhook Gateway and Email Server are local, we can have them share a secret token with orchestrator (set in env) and include it in HTTP headers. Alternatively, running everything behind a firewall or on a closed network interface is acceptable (per the current design, the gateway was going to run on localhost for Google to push to, perhaps with an SSH tunnel or similar – orchestrator would similarly run on localhost).

Logging, Memory & Database Schema Details

We have touched on these, but to summarize key points and ensure completeness:

Logging: Use Python’s logging module. Configure it in config.py or utils.py to output timestamps and log levels. We might set it to INFO by default, with DEBUG for more verbose output when needed. Each log entry should include context when available (like problem_id and tool name). For example:

import logging logger = logging.getLogger("MCP_Orchestrator") logger.setLevel(logging.INFO)

configure handler, formatter, etc.

logger.info(f"[Problem {pid}] Tool selected: {tool_name} (perm={perm})")

We will also log errors and exceptions with stack traces to help diagnose issues.

Vector Memory (Qdrant): We'll create a Qdrant collection agent_tools with vector size equal to our embedding dimension (e.g., 768) and cosine distance metric GitHub . Each tool will be upserted with an id equal to maybe the tool name or a UUID, payload containing metadata (tool_name, permission level, tags), and the vector. The orchestrator will use Qdrant’s filtering and search API: for instance, to only search among tools where action_permission != 'permission_required' if we want to avoid suggesting those when fully autonomous. (This was noted as a capability GitHub and we can implement it via Qdrant’s filtering by payload conditions.)

For long-term memory, we create another collection, maybe assistant_memory. For each memory item, we store the vector and payload with fields like user_id, problem_id (if related to a specific conversation), text (the actual content or a reference). We might not implement complex forgetting or chunking in v1, but even storing key points could help later.

The orchestrator will ensure that adding to vector DB is done asynchronously (so as not to block responses). For example, after a problem is solved, we could spawn a task to summarize the whole conversation and store that vector for the future, without delaying the final response to the user.

PostgreSQL Schema: If not already existing, we will include a migration or SQL script to create needed tables. Example schema (SQL):

CREATE TABLE problems ( id SERIAL PRIMARY KEY, user_id TEXT, title TEXT, status TEXT, -- e.g., 'in_progress', 'awaiting_user', 'done', 'canceled' problem_type TEXT DEFAULT 'general', context JSONB DEFAULT '{}'::jsonb, definition_of_done TEXT, created_at TIMESTAMP DEFAULT NOW(), updated_at TIMESTAMP DEFAULT NOW() ); CREATE TABLE problem_steps ( id SERIAL PRIMARY KEY, problem_id INTEGER REFERENCES problems(id) ON DELETE CASCADE, step_number INTEGER, actor TEXT, -- 'user' or 'agent' action TEXT, -- e.g., 'request', 'tool_selected', 'tool_executed', 'confirmation', 'completed', etc. description TEXT, detail JSONB, created_at TIMESTAMP DEFAULT NOW() ); CREATE INDEX idx_problem_active ON problems(user_id, status) WHERE status IN ('in_progress','awaiting_user','blocked');

And for tools if needed:

CREATE TABLE agent_tools ( tool_name TEXT PRIMARY KEY, description TEXT, parameter_definitions JSONB, action_permission TEXT, tool_type TEXT, embedding_vector_id TEXT -- to link with Qdrant, store the UUID of the vector if needed );

These align with what PA_V2 had GitHub and what our orchestrator needs. The orchestrator will use parameterized queries or an ORM (like SQLAlchemy or Tortoise ORM if we want async) to interact with these tables. Given the scale (not a huge load), even simple direct queries in asyncpg are fine.

Maintaining Conversations: The orchestrator won’t store entire conversation history in memory like the Telegram bot did with conversation.jsonl files. Instead, short-term history can be kept in memory per user session (like last few messages) if needed for the LLM context, and anything long-term we offload to Postgres/Qdrant. Since the local LLM is 8B, it might have a limited context window (maybe 4096 tokens if based on Llama2 8B). We can dynamically construct prompts from the stored info: e.g., include the last 2 user messages and assistant responses from the DB if available, plus any relevant memory fetched from Qdrant. This way, the assistant has context without needing to carry a huge conversation buffer in RAM.

Performance: Using local components (LLM and Qdrant), we must be mindful of performance:

The LLM inference on 8B Q5_K model can be a few tokens per second on CPU. If a GPU is available, it should be used (perhaps load 8-bit layers to GPU if possible). We may also limit the length of responses and use caching for repeated tasks.

Qdrant queries are fast (millisecond-level for our scale) GitHub , so that’s fine.

Postgres calls should be small and indexed. Using async allows concurrency (the orchestrator can handle multiple requests in parallel if needed, though initial use might be single-user focused).

We should initialize heavy resources at startup (model, DB connections) so that the first request doesn’t incur setup cost. The orchestrator might even run a self-test on launch (embedding a sample query, doing a vector search, trivial LLM prompt) to ensure components are ready.

Security & Deployment Considerations

Given that PA_MCP_Orchestrator_Server_V1 has access to powerful tools (like sending emails, potentially controlling accounts, running code via tools), it’s critical to deploy it in a secure environment and follow best practices:

Local Deployment: The orchestrator is intended to run on a local server or network, not exposed to the public internet. In HTTP mode, bind it to 127.0.0.1 or a secure LAN IP. If it must accept connections from other hosts, restrict those hosts (firewall or allow-list IPs of your other services). Use an SSH tunnel or VPN if you need to connect a remote Telegram gateway to it.

Authentication: Optionally, implement a simple authentication for HTTP endpoints (since all clients are known internal services, a shared secret token could suffice). For example, require an Authorization: Bearer header on requests, where the token is set in the environment (in secrets.txt). The orchestrator can check this on each request and reject any that don’t match. This prevents an outsider on the network from calling the orchestrator’s tools.

Secrets Management: As mentioned, the orchestrator will load credentials from secrets.txt or environment variables:

Database credentials (host, user, password) – these should be stored securely (the secrets.txt presumably contains or points to these).

Qdrant API Key – if Qdrant requires one (it might not in a local setup).

LLM Model Path – not sensitive but good to have configurable.

OpenAI API key – if provided, stored in secrets but orchestrator should only use it if configured to do so (and even then, possibly only for certain functions like embeddings, to keep costs down).

Other API keys – e.g., weather API key, etc., can also be put in env or a config file.

The orchestrator will not write these secrets to logs or expose them. When building Docker images or deploying, ensure the secrets.txt is injected via environment or volume, not baked into the image.

Resource Usage: Running an 8B model and Qdrant on one machine means we should monitor CPU/RAM. It's wise to run orchestrator on a machine with a decent amount of RAM (the model could use ~4-8GB in 8-bit mode) and possibly a GPU. The config should allow specifying number of threads for the model to avoid using 100% CPU if multiple requests come.

Deployment: You can run the orchestrator in multiple ways:

As a simple process: e.g., uvicorn pa_mcp_orchestrator.main:app --host 127.0.0.1 --port 8000 for HTTP mode. Or python -m pa_mcp_orchestrator.main --stdio for stdio mode. We provide a orchestrator.service example for systemd, which ensures it starts on boot, restarts on failure, and logs to the system journal. Adjust the service file to point to the correct working directory, user, and environment file for secrets.

Docker: The Dockerfile can be used to containerize the orchestrator with all its dependencies. For example, build an image that includes the model file (or mounts it at runtime). When running, bind mount the secrets and model as needed. Running in Docker might complicate accessing the model (size) and GPU usage, so in development it might be easier to run on the host.

Client Configuration: Other services need to know how to reach the orchestrator. For the Webhook Gateway, we would set MCP_ENDPOINT_URL="http://127.0.0.1:8000/tool/call" (if orchestrator on same machine) and MCP_MODE="HTTP" GitHub . For the Email Server, similarly, configure it to call orchestrator via HTTP. If any service is in STDIO mode, provide the path to the orchestrator’s executable and perhaps use MCP_EXECUTABLE config as the gateway’s .env example shows GitHub .

Testing: Before deploying to production, test each interaction path:

Simulate a Telegram message (call the API manually or via a test client) and see that orchestrator returns the expected JSON.

Simulate an incoming email event (call the orchestrator’s analyze function with a sample email payload).

Test a permission-required tool by calling orchestrator directly and verifying it does not execute without confirmation.

Test the LLM reasoning by giving it a couple of sample prompts and see if it chooses sensible tools (this can be done offline by calling an internal function).

Comprehensive testing will ensure the orchestrator behaves reliably when it’s live.

Updating Tools: When adding new tools or updating descriptions, remember to update the vector index. The orchestrator could provide an admin endpoint or script to reload tools (for example, if tool_definitions.yaml is changed, run a script to re-ingest tools to DB and Qdrant). We might automate this on startup: e.g., if the DB is empty or a flag is set, load the YAML. Otherwise, assume tools are already in DB. This prevents wiping out embeddings on every run unnecessarily. A separate populate_tools.py script (like in PA_V2) can be included for initial setup.

Avoiding Code Duplication: We explicitly avoid copying large portions of PA_V2 code; instead we take the lessons and implement afresh in a cleaner way. For instance, the enhanced tool routing summary GitHub outlines features like fallbacks and analytics which we can design in, but we’ll write new code for them. By not entangling with legacy code, this new orchestrator remains lean.

Conclusion

PA_MCP_Orchestrator_Server_V1 will be the intelligent coordinator that brings all components of the personal assistant together. With a clean modular structure, robust use of local AI models, and proper integration of memory and databases, it is well-positioned to handle current needs (email management, Telegram Q&A, scheduling) and scale to future capabilities (additional platforms and automated workflows). The above design provides a blueprint for implementation, ensuring that the final system is MCP-protocol compatible, secure, and maintainable. By reusing existing credentials and focusing on local processing, we minimize external dependencies while maximizing user privacy and control.

Once implemented, this orchestrator will allow the user to interact with their AI assistant seamlessly – whether it’s reading an email, getting a reminder, or performing a multi-step task – all driven by a central “brain” that can reason and act across all these domains. The next steps would be to proceed with coding each module as described, writing unit tests for the core logic (especially tool selection and HITL flows), and then integrating it with the existing PA services one by one. With careful testing and iteration, PA_MCP_Orchestrator_Server_V1 will become the cornerstone of the Personal Assistant v3 architecture, enabling a smarter and more unified assistant experience.