olafgeibig/knowledge-mcp

knowledge-mcp: Specialized Knowledge Bases for AI Agents

1. Overview and Concept

knowledge-mcp is a MCP server designed to bridge the gap between specialized knowledge domains and AI assistants. It allows users to create, manage, and query dedicated knowledge bases, making this information accessible to AI agents through an MCP (Model Context Protocol) server interface.

The core idea is to empower AI assistants that are MCP clients (like Claude Desktop or IDEs like Windsurf) to proactively consult these specialized knowledge bases during their reasoning process (Chain of Thought), rather than relying solely on general semantic search against user prompts or broad web searches. This enables more accurate, context-aware responses when dealing with specific domains.

Key components:

• CLI Tool: Provides a user-friendly command-line interface for managing knowledge bases (creating, deleting, adding/removing documents, configuring, searching).
• Knowledge Base Engine: Leverages LightRAG to handle document processing, embedding, knowledge graph creation, and complex querying.
• MCP Server: Exposes the search functionality of the knowledge bases via the FastMCP protocol, allowing compatible AI agents to query them directly.

2. About LightRAG

This project utilizes LightRAG (HKUDS/LightRAG) as its core engine for knowledge base creation and querying. LightRAG is a powerful framework designed to enhance Large Language Models (LLMs) by integrating Retrieval-Augmented Generation (RAG) with knowledge graph techniques.

Key features of LightRAG relevant to this project:

• Document Processing Pipeline: Ingests documents (PDF, Text, Markdown, DOCX), chunks them, extracts entities and relationships using an LLM, and builds both a knowledge graph and vector embeddings.
• Multiple Query Modes: Supports various retrieval strategies (e.g., vector similarity, entity-centric, relationship-focused, hybrid) to find the most relevant context for a given query.
• Flexible Storage: Can use different backends for storing key-value data, vectors, graph information, and document status (this project uses the default file-based storage).
• LLM/Embedding Integration: Supports various providers like OpenAI (used in this project), Ollama, Hugging Face, etc.

By using LightRAG, knowledge-mcp benefits from advanced RAG capabilities that go beyond simple vector search.

3. Installation

Ensure you have Python 3.12 and uv installed.

1. Running the Tool: After installing the package (e.g., using uv pip install -e .), you can run the CLI using uvx:

   # General command structure
   uvx knowledge-mcp --config <path-to-your-config.yaml> <command> [arguments...]
   
   # Example: Start interactive shell
   uvx knowledge-mcp --config <path-to-your-config.yaml> shell
   

2. Configure MCP Client: To allow an MCP client (like Claude Desktop or Windsurf) to connect to this server, configure the client with the following settings. Replace the config path with the absolute path to your main config.yaml.

   {
     "mcpServers": {
       "knowledge-mcp": {
         "command": "uvx",
         "args": [
           "knowledge-mcp",
           "--config",
           "<absolute-path-to-your-config.yaml>",
           "mcp"
         ]
       }
     }
   }
   

3. Set up configuration:

   • Copy config.example.yaml to config.yaml.
   • Copy .env.example to .env.
   • Edit config.yaml and .env to add your API keys (e.g., OPENAI_API_KEY) and adjust paths or settings as needed. The knowledge_base.base_dir in config.yaml specifies where your knowledge base directories will be created.

4. Configuration

Configuration is managed via YAML files:

1. Main Configuration (config.yaml): Defines global settings like the knowledge base directory (knowledge_base.base_dir), LightRAG parameters (LLM provider/model, embedding provider/model, API keys via ${ENV_VAR} substitution), and logging settings. Refer to config.example.yaml for the full structure and available options.

   knowledge_base:
     base_dir: ./kbs
   
   lightrag:
     llm:
       provider: "openai"
       model_name: "gpt-4.1-nano"
       api_key: "${OPENAI_API_KEY}"
       # ... other LLM settings
     embedding:
       provider: "openai"
       model_name: "text-embedding-3-small"
       api_key: "${OPENAI_API_KEY}"
       # ... other embedding settings
     embedding_cache:
       enabled: true
       similarity_threshold: 0.90
   
   logging:
     level: "INFO"
     # ... logging settings
   
   env_file: .env # path to .env file
   

2. Knowledge Base Specific Configuration (<base_dir>/<kb_name>/config.yaml): Contains parameters specific to querying that knowledge base, such as the LightRAG query mode, top_k results, context token limits, etc. This file is automatically created with defaults when a KB is created and can be viewed/edited using the config CLI command.

3. Knowledge Base Directory Structure: When you create knowledge bases, they are stored within the directory specified by knowledge_base.base_dir in your main config.yaml. The structure typically looks like this:

   <base_dir>/              # Main directory, contains a set of knowledge bases
   ├── config.yaml          # Main application configuration (copied from config.example.yaml)
   ├── .env                 # Environment variables referenced in config.yaml
   ├── kbmcp.log
   ├── knowledge_base_1/    # Directory for the first KB
   │   ├── config.yaml      # KB-specific configuration (query parameters)
   │   ├── <storage_files>  # The LightRAG storage files
   └── knowledge_base_2/    # Directory for the second KB
       ├── config.yaml
       ├── <storage_files>
   

5. Usage (CLI)

The primary way to interact with knowledge-mcp is through its CLI, accessed via the knowledge-mcp command (if installed globally or via uvx knowledge-mcp within the activated venv).

All commands require the --config option pointing to your main configuration file.

uv run knowledge-mcp --config /path/to/config.yaml shell

Available Commands (Interactive Shell):

Example (Direct CLI):

# Create a knowledge base named 'my_docs'
knowledge-mcp --config config.yaml create my_docs

# Add a document to it
knowledge-mcp --config config.yaml add my_docs ./path/to/mydocument.pdf

# Search the knowledge base
knowledge-mcp --config config.yaml query my_docs "What is the main topic?"

# Start the interactive shell
knowledge-mcp --config config.yaml shell

(kbmcp) list
(kbmcp) query my_docs "Another query"
(kbmcp) exit

6. Development

1. Project Decisions

• Tech Stack: Python 3.12, uv (dependency management), hatchling (build system), pytest (testing).
• Setup: Follow the installation steps, ensuring you install with uv pip install -e ".[dev]".
• Code Style: Adheres to PEP 8.
• Testing: Run tests using uvx test or pytest.
• Dependencies: Managed in pyproject.toml. Use uv pip install <package> to add and uv pip uninstall <package> to remove dependencies, updating pyproject.toml accordingly.
• Scripts: Common tasks might be defined under [project.scripts] in pyproject.toml.
• Release: Build hatch build and then twine upload dist/*.

2. Test with uvx

    "knowledge-mcp": {
      "command": "uvx",
      "args": [
        "--project",
        "/path/to/knowledge-mcp",
        "knowledge-mcp",
        "--config",
        "/path/to/knowledge-mcp/kbs/config.yaml",
        "mcp"
      ]
    }

3. Test with MCP Inspector

npx @modelcontextprotocol/inspector uv "run knowledge-mcp --config /path/to/config.yaml mcp"

or

npx @modelcontextprotocol/inspector uvx --project . knowledge-mcp "--config ./kbs/config.yaml mcp

4. Convenience dev scripts Assumes a local config file at ./kbs/config.yaml

• uvx shell - Starts the interactive shell
• uvx insp - Starts the MCP Inspector