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
@MCP-Mirror
Basic information
About Cognee MCP Server
Mirror of
Cognee MCP Server config
No standard config provided
This server doesn't expose a parseable MCP config block in its README. See the repository for install instructions.
RepositoryCognee MCP Server tools
1Builds knowledge graph from the input text and performs search in it.
Overview
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
Frequently asked questions
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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