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Documentation Index

Fetch the complete documentation index at: https://docs.cognee.ai/llms.txt

Use this file to discover all available pages before exploring further.

Cognee runs as an MCP (Model Context Protocol) server. Any MCP-compatible client (Claude Desktop, Cursor, VS Code Copilot) can connect to it as a tool provider. The MCP server can run locally or connect to your Cognee Cloud tenant.

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Step 1 — Start the MCP server

Connect the MCP server to Cognee Cloud using your API Base URL and API key from the API Keys page: 
cognee-mcp --transport sse --port 8001 \
  --serve-url https://your-tenant.aws.cognee.ai \
  --serve-api-key your-api-key
For local mode (no Cloud connection), omit the --serve-url and --serve-api-key flags. The server will manage its own local knowledge graph. Local mode requires an LLM_API_KEY environment variable.

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Step 2 — Add to your MCP client config

Add Cognee as a tool server in your MCP client’s configuration: 
{
  "mcpServers": {
    "cognee": {
      "url": "http://localhost:8001/sse"
    }
  }
}

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Step 3 — Available tools

Once connected, your MCP client gets these tools:
ToolDescription
rememberStore data in memory (add + cognify in one step)
recallSearch memory with auto-routing
cognifyBuild the knowledge graph from ingested data
searchQuery the knowledge graph with different search types
improveEnrich the knowledge graph and bridge session data
forget_memoryDelete data from memory
list_dataList datasets and their contents
For detailed setup per client, see the MCP integration guides.

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Next steps

Cloud SDK

Connect to Cognee Cloud programmatically using the Python SDK.

Cloud functionality

Explore the full API surface available in Cognee Cloud.