What tool does cognee-mcp-server provide?
It provides the `Cognify_and_search` tool that builds a knowledge graph from input text and performs search in it, returning retrieved edges of the graph.
Cognee MCP Server
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1Builds knowledge graph from the input text and performs search in it.
概要
What is cognee-mcp-server?
cognee-mcp-server is an MCP server for cognee, an AI memory engine. It provides a tool that builds a knowledge graph from input text and performs search within that graph, intended for developers integrating memory capabilities into AI assistants.
How to use cognee-mcp-server?
Install with uv (Python package manager) and configure in Claude Desktop by adding an entry to claude_desktop_config.json using uvx. Invoke the Cognify_and_search tool with required inputs text and search_query, and optional graph_model_file and graph_model_name for a custom Pydantic graph model.
Key features of cognee-mcp-server
- Builds knowledge graphs from input text
- Performs search within constructed knowledge graphs
- Supports custom Pydantic graph models
- Configurable with Claude Desktop via uvx
- Uses multiple database providers (NetworkX, LanceDB, SQLite)
Use cases of cognee-mcp-server
- Semantic search over user-provided text data
- Queryable memory for AI assistants
- Knowledge graph retrieval from custom content
よくある質問
How do I configure cognee-mcp-server for Claude Desktop?
Add a configuration entry to `claude_desktop_config.json` using the `uv` command with the `--directory` flag pointing to the server path, and set required environment variables such as `LLM_API_KEY`, `GRAPH_DATABASE_PROVIDER`, `VECTOR_DB_PROVIDER`, `DB_PROVIDER`, and `DB_NAME`.
What are the required environment variables?
Required environment variables include `ENV`, `TOKENIZERS_PARALLELISM`, `LLM_API_KEY`, `GRAPH_DATABASE_PROVIDER` (e.g., `networkx`), `VECTOR_DB_PROVIDER` (e.g., `lancedb`), `DB_PROVIDER` (e.g., `sqlite`), and `DB_NAME` (e.g., `cognee_db`).
Can I use a custom graph model?
Yes, you can provide optional `graph_model_file` (filename of a custom Pydantic graph model implementation) and `graph_model_name` (class name) to customize the graph model.
What output does the `Cognify_and_search` tool produce?
The tool returns the retrieved edges of the knowledge graph as output.
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