Memory/Knowledge Graph MCP Server for IDE Assistants - Persistent context and knowledge for AI coding agents
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# MCP-KG-Memory Server Python MCP server implementation with Neo4j backend. ## Development Setup ```bash # Create virtual environment python -m venv .venv source .venv/bin/activate # Install dependencies (including dev) pip install -e ".[dev]" # Run tests pytest tests/ -v # Run server python -m kg_mcp.main ``` ## Project Structure ``` src/kg_mcp/ ├── main.py # Entry point ├── config.py # Settings management ├── llm/ # LLM integration │ ├── client.py # LiteLLM wrapper │ ├── schemas.py # Pydantic models │ └── prompts/ # Prompt templates ├── kg/ # Knowledge graph │ ├── neo4j.py # Driver/client │ ├── schema.cypher # DB schema │ ├── repo.py # Query repository │ ├── ingest.py # Ingestion pipeline │ └── retrieval.py # Context builder ├── mcp/ # MCP components │ ├── tools.py # Tool definitions │ ├── resources.py # Resource handlers │ └── prompts.py # Prompt templates ├── codegraph/ # Code indexing (V1) │ ├── model.py # Data models │ └── indexer.py # File indexer └── security/ # Auth/Origin ├── auth.py # Token validation └── origin.py # Origin checking ```
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