elevend0g/cont3xt
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cont3xt is a Model Context Protocol (MCP) server designed to manage and optimize context for language models and agents, ensuring relevant information is always available without exceeding token limits.
cont3xt
Virtual Infinite Context for Agents and LLMs
An MCP server that maintains a continuous rolling context window, always surfacing the most relevant memories for the task at hand — while respecting your token budget.
Current Status
This repository includes an MVP MCP stdio server suitable for integration with the Cline extension for VS Code. It exposes a minimal, SQLite-only toolset over stdio with zero external services required by default.
- Version: 0.1.1
- Transport: MCP stdio
- Default storage: SQLite at ./data/memory.db (configurable; example config uses ./data/novel_memory.db)
- Optional backends: Qdrant / Neo4j (disabled by default in this MVP)
- Tools implemented:
- memory_upsert
- search_memory
- context_pack
- health_ping
Notes:
- Tool names use underscores for compatibility with the Python server decorator. Dotted aliases (e.g., context.pack) can be added later.
- All tools return a standard JSON envelope as text content: { "ok": boolean, "data": {...}, "meta": { "duration_ms": number, "backend": "sqlite", "version": "0.1.1" } }
Quickstart
1) Install
Requires Python 3.11+
git clone https://github.com/elevend0g/cont3xt.git
cd cont3xt
pip install -r requirements.txt
pip install -e .
2) Initialize local storage (SQLite)
virtual-context-mcp --init-db
This creates ./data/memory.db if it doesn’t exist (or the path specified by config/env).
3) Run (stdio transport)
virtual-context-mcp
This starts the MCP server over stdio.
Using with MCP Clients
Cline (VS Code)
Add an MCP server named cont3xt that launches the stdio server:
Example configuration shape (adapt for your Cline settings UI/JSON):
{
"mcpServers": {
"cont3xt": {
"command": "virtual-context-mcp",
"args": ["--config", "configs/novel_writing.yaml"],
"env": {
"CONTEXT_MAX_TOKENS": "12000",
"PYTHONUNBUFFERED": "1"
}
}
}
}
Notes:
- Ensure virtual-context-mcp is in PATH (pip install -e . creates the console script).
- The --config argument is optional; environment variables can override values (see Configuration).
Claude Desktop
{
"mcpServers": {
"cont3xt": {
"command": "virtual-context-mcp",
"args": ["--config", "configs/novel_writing.yaml"],
"env": {
"CONTEXT_MAX_TOKENS": "12000",
"PYTHONUNBUFFERED": "1"
}
}
}
}
Tools (MVP)
All tools return a JSON envelope as text content.
Envelope:
{
"ok": true,
"data": { /* tool-specific payload */ },
"meta": {
"duration_ms": 5.23,
"backend": "sqlite",
"version": "0.1.1"
}
}
1) memory_upsert
Upsert memory for a session from either combined content or a user/assistant pair.
Arguments:
- session_id: string (required)
- content: string (optional; direct content)
- user_input: string (optional; used if content not provided)
- assistant_response: string (optional; used if content not provided)
Response (example):
{
"ok": true,
"data": {
"session_id": "sess-1",
"ids": ["b1c...f"],
"count": 1
},
"meta": {
"duration_ms": 1.23,
"backend": "sqlite",
"version": "0.1.1"
}
}
2) search_memory
Simple substring search with a recency-biased score over recent SQLite chunks.
Arguments:
- session_id: string (required)
- query: string (required)
- max_results: number (optional, default 10)
Response (example):
{
"ok": true,
"data": {
"query": "emerald eyes",
"results_count": 2,
"results": [
{
"chunk_id": "b1c...f",
"score": 1.7,
"timestamp": "2025-08-09T17:30:00.000000",
"token_count": 142,
"preview": "User: ... Assistant: ..."
}
]
},
"meta": {
"duration_ms": 2.45,
"backend": "sqlite",
"version": "0.1.1"
}
}
3) context_pack
Packs a token-budgeted context using recent conversation chunks. Reserves a small buffer and includes the current input if provided.
Arguments:
- session_id: string (required)
- current_input: string (optional, default "")
- budget_tokens: number (optional, defaults to CONTEXT_MAX_TOKENS)
Response (example):
{
"ok": true,
"data": {
"pack": {
"schema_version": "ctx.v1",
"budget_tokens": 12000,
"used_tokens": 2834,
"sections": [
{"role": "user", "title": "Current Input", "content": "..."},
{
"role": "context",
"title": "Conversation (2025-08-09T17:30:00.000000)",
"content": "...",
"chunk_id": "b1c...f",
"token_count": 142
}
],
"provenance": {
"retriever": "recent-only",
"stores": {"sqlite": "./data/memory.db"}
},
"pack_stats": {
"num_candidates": 12,
"kept": 4,
"dropped": 8,
"buffer_tokens": 500
}
},
"meta": {
"input_tokens": 120,
"output_tokens": 2834,
"budget": 12000,
"sources": ["b1c...f", "a9d...1"]
}
},
"meta": {
"duration_ms": 7.89,
"backend": "sqlite",
"version": "0.1.1"
}
}
Notes:
- If nothing fits within the budget (after a fixed buffer of 500 tokens), the pack includes:
"reason": "BUDGET_TOO_SMALL". - Truncation policy: respect budget strictly; sections are assembled most-recent-first, then presented chronologically for readability.
4) health_ping
Returns basic status and configuration fingerprint.
Arguments: none
Response (example):
{
"ok": true,
"data": {
"server": "cont3xt",
"version": "0.1.1",
"datetime": "2025-08-09T17:32:00.000000",
"config": {
"sqlite_path": "./data/memory.db",
"max_tokens": 12000,
"token_model": "cl100k_base",
"optional_backends": {
"qdrant": false,
"neo4j": false
}
}
},
"meta": {
"duration_ms": 0.41,
"backend": "sqlite",
"version": "0.1.1"
}
}
Cline Demo Task (Code Gen Flow)
- In Cline, run a task like “Improve function X in file Y; add a unit test and make tests pass.”
- Cline should:
- Call
search_memory(session_id, query="project goals") - Call
context_pack(session_id, current_input="<file diff request>", budget_tokens=12000) - Generate plan → apply small diff → run tests
- Call
memory_upsertwith(user_input, assistant_response)to persist the session outcome
- Call
- Acceptance:
- From a clean clone,
pip install -e ., launch Cline, and complete a guided code edit PR in one go using the above flow.
- From a clean clone,
Configuration
Defaults are provided in code, optionally loaded from a YAML file (e.g., configs/novel_writing.yaml) and overridden by environment variables.
Environment variable overrides (examples):
-
CONTEXT_MAX_TOKENS (default 12000)
-
CONTEXT_PRESSURE_THRESHOLD (default 0.8)
-
CONTEXT_RELIEF_PERCENTAGE (default 0.4)
-
CONTEXT_CHUNK_SIZE (default 3200)
-
CONTEXT_TOKEN_MODEL (default cl100k_base)
-
DATABASE_SQLITE_PATH (default ./data/memory.db)
-
DATABASE_QDRANT_URL
-
DATABASE_QDRANT_COLLECTION
-
DATABASE_NEO4J_URL
-
DATABASE_NEO4J_USER
-
DATABASE_NEO4J_PASSWORD
For the MVP, only SQLite is used. Qdrant / Neo4j are intentionally not required; future versions can enable them via env/config flags.
Truncation policy:
- A fixed buffer of 500 tokens is reserved.
- Packing order: include current_input (if provided) and then pack recent conversation chunks until the budget is met.
- If no sections can be included, the pack includes
"reason": "BUDGET_TOO_SMALL".
Development
Entry points:
- Console script: virtual-context-mcp → virtual_context_mcp.main:main
- MCP server class: virtual_context_mcp.server:VirtualContextMCPServer
Local logs: standard output (set PYTHONUNBUFFERED=1 for real-time logs). SQLite file: ./data/memory.db by default.
Run unit/integration tests as needed (note that legacy story-focused modules may not reflect the MVP surface area).
Security
- MVP runs in a safe default mode: only persists to local SQLite.
- No filesystem ingestion or external network writes by default.
Roadmap
- Optional Qdrant/Neo4j integration behind env flags with graceful fallback.
- Aliased/dotted tool names for compatibility (e.g., context.pack).
- Richer retrieval (hybrid, deduplication), entity/graph memory, and pressure relief policies.
- DevContainer / Docker image for reproducible environments.
License
MIT © elevend0g