What does the Cognify_and_search tool do?
It constructs a knowledge graph from the provided text and then performs a search using the given query, returning the matching edges of the graph.
Cognee MCP Server
@topoteretes
Basic information
About Cognee MCP Server
No overview available yet
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?
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, returning retrieved edges.
How to use cognee-mcp-server?
Configure it in your Claude Desktop client (claude_desktop_config.json) using uvx. Set the required environment variables (e.g., LLM_API_KEY, GRAPH_DATABASE_PROVIDER, VECTOR_DB_PROVIDER, DB_PROVIDER, DB_NAME). Invoke the Cognify_and_search tool with the text and search query as inputs.
Key features of cognee-mcp-server
- Builds a knowledge graph from input text
- Performs search in the constructed graph
- Returns retrieved edges of the knowledge graph
- Supports optional custom Pydantic graph models
Use cases of cognee-mcp-server
- Ingesting unstructured text and answering queries over it
- Building a searchable memory layer for AI applications
Frequently asked questions
Does cognee-mcp-server support custom graph models?
Yes. You can optionally provide a `graph_model_file` (filename of a custom Pydantic graph model implementation) and `graph_model_name` (the class name) to customize the graph structure.
What environment configuration is required?
You must set `LLM_API_KEY`, `GRAPH_DATABASE_PROVIDER`, `VECTOR_DB_PROVIDER`, `DB_PROVIDER`, and `DB_NAME`. Example values: networkx, lancedb, sqlite.
What runtime dependencies does it have?
It requires Python and uv (for running via uvx). The optional custom graph model feature depends on a Pydantic implementation.
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