A Model Context Protocol server for managing a graph of reusable Python functions, documentation, and environment variables
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# MCP Knowledge Graph Skills A Model Context Protocol (MCP) server that manages a graph of reusable Python functions, documentation, and environment variables. Claude can dynamically compose and execute scripts by importing functions from the graph. ## Features - **Graph-Based Knowledge Management**: Organize skills, scripts, documentation, and environments in a Neo4j knowledge graph - **Dynamic Script Composition**: Import and combine Python functions at execution time - **Automatic Dependency Management**: PEP 723 inline script metadata with uv-powered execution - **Secret Protection**: Automatic detection and sanitization of sensitive environment variables - **Relationship Tracking**: Connect related skills and resources with CONTAINS and RELATE_TO relationships - **Flexible Querying**: Explore the knowledge graph using read-only Cypher queries ## Quick Start Get started in 3 steps: ```bash # 1. Install uv (if not already installed) curl -LsSf https://astral.sh/uv/install.sh | sh # 2. Start Neo4j (using Docker) docker run -d --name neo4j -p 7687:7687 -e NEO4J_AUTH=neo4j/password neo4j:latest # 3. Create config file mkdir -p ~/.mcp-kg-skills/config cat > ~/.mcp-kg-skills/config/database.yaml << 'EOF' database: uri: "bolt://localhost:7687" username: "neo4j" password: "password" database: "neo4j" execution: cache_dir: "~/.mcp-kg-skills/cache" env_dir: "~/.mcp-kg-skills/envs" default_timeout: 300 max_timeout: 600 security: secret_patterns: - "^SECRET_" - "_SECRET$" - "^.*_KEY$" - "^.*_PASSWORD$" - "^.*_TOKEN$" logging: level: "INFO" EOF # 4. Run the server uvx mcp-kg-skills ``` That's it! Now configure your MCP client (see [MCP Client Configuration](#mcp-client-configuration)). ## Architecture ``` ┌─────────────────────────────────────────────────────┐ │ LLM (Claude via MCP Client) │ └─────────────────┬───────────────────────────────────┘ │ MCP Protocol (FastMCP 2.10) ┌────…
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