Cognee Cloud & MCP

Connecting MCP to Cognee Cloud

The Cognee MCP server can connect to your Cognee Cloud tenant using the --api-url and --api-token flags, with the API Base URL and API key from your API Keys page. This lets any MCP-compatible client (Claude Desktop, Cursor, VS Code Copilot) work with your cloud-hosted knowledge graph.

	Cognee MCP	Cognee Cloud
Where it runs	Locally on your machine	Hosted by Cognee
Access method	MCP protocol	REST API via `--api-url`, or Cloud UI
Authentication	Bearer token (self-hosted)	`X-Api-Key` header
API endpoints	Self-hosted backend endpoints such as `/api/v1/remember` and `/api/v1/recall`	The same REST endpoints on your tenant’s API Base URL
Use case	AI IDE tools (Cursor, Claude Code, etc.)	Cloud-managed knowledge graphs

How API mode connects to a backend

The MCP server’s --api-url / --api-token options put it in API mode: instead of running pipelines locally, the MCP tools send their requests to the Cognee backend at that URL. API mode works with both self-hosted backends and Cognee Cloud tenants:

Self-hosted backends authenticate with Authorization: Bearer <token>
Cognee Cloud tenant URLs are detected automatically and authenticate with the X-Api-Key header

The --serve-url / --serve-api-key flags are a different mechanism: they call cognee.serve() to route SDK operations, and they do not configure the API client the MCP tools use — so a Cloud connection configured only with --serve-url leaves some tools working against local storage. For Cloud connections, use --api-url / --api-token.

End-to-end walkthrough

Get your API credentials

Open the API Keys page in the Cognee Cloud console. Copy:

API Base URL — looks like https://your-tenant.aws.cognee.ai
API Key — a long token used to authenticate requests

Start the MCP server

Pass your credentials via CLI flags:

cognee-mcp --transport sse --port 8001 \
  --api-url https://your-tenant.aws.cognee.ai \
  --api-token your-api-key

The server is ready when you see output like:

INFO: Started server process
INFO: Waiting for connections on http://127.0.0.1:8001

Add Cognee to your MCP client

Add the server to your client’s MCP configuration. The config file location varies by client — see the integrations section for your specific tool.

{
  "mcpServers": {
    "cognee": {
      "url": "http://localhost:8001/sse"
    }
  }
}

Verify the connection

Test that memory operations reach your Cloud tenant. In your MCP client, ask:

“Use Cognee to remember: cloud connection test successful”

Then retrieve it:

“Use Cognee to recall the cloud connection test”

If Cognee returns the stored value, the end-to-end connection is working. You can also confirm the data appeared in the Cognee Cloud UI.

Available tools

Once connected, your MCP client gets the Cognee API v1 memory tools:

Tool	Description
`remember`	Store data in memory (add + cognify in one step)
`recall`	Search memory with auto-routing
`forget`	Delete data from memory

Other connection options

Use Cognee MCP for local AI memory (standalone)

Run the MCP server in standalone mode (no Cloud connection). The server manages its own local knowledge graph.

# Standalone mode — requires LLM_API_KEY
LLM_API_KEY=sk-... cognee-mcp --transport sse --port 8001

This is the simplest way to add persistent memory to Cursor, Claude Code, Cline, and other MCP-compatible tools.

Use the Python SDK instead

Access Cognee Cloud programmatically using the cognee SDK connected through serve(), which handles authentication and communication with the hosted service.

export COGNEE_SERVICE_URL="https://your-tenant.aws.cognee.ai"
export COGNEE_API_KEY="your-cognee-cloud-api-key"

import asyncio
import cognee

async def main():
    await cognee.serve()  # Reads COGNEE_SERVICE_URL and COGNEE_API_KEY
    await cognee.remember("...", dataset_name="my_dataset")
    results = await cognee.recall("...", datasets=["my_dataset"])
    print(results)

asyncio.run(main())

If you prefer not to use environment variables, pass both values directly:

await cognee.serve(
    url="https://your-tenant.aws.cognee.ai",
    api_key="your-api-key",
)

See the Cloud SDK guide for a complete walkthrough.

Connect MCP to a self-hosted Cognee backend

If you want multiple AI clients to share a single knowledge graph, run a self-hosted Cognee backend and point the MCP server at it using API_URL and API_TOKEN:

# 1. Start a self-hosted Cognee backend
docker run -e LLM_API_KEY=your_key -p 8080:8000 --rm -it cognee/cognee:main

# 2. Start MCP in API mode pointing to your backend
docker run \
  -e TRANSPORT_MODE=sse \
  -e API_URL=http://localhost:8080 \
  -e API_TOKEN=your_backend_token \
  -p 8000:8000 --rm -it cognee/cognee-mcp:main

See the MCP Quickstart for full details on this pattern.

Setup

Integrations

Connecting MCP to Cognee Cloud

How API mode connects to a backend

End-to-end walkthrough

Available tools

Other connection options

​Connecting MCP to Cognee Cloud

​How API mode connects to a backend

​End-to-end walkthrough

​Available tools

​Other connection options

Connecting MCP to Cognee Cloud

How API mode connects to a backend

End-to-end walkthrough

Available tools

Other connection options