Building a Production-Ready FastMCP Server Integration for Cognee Knowledge Graphs

text
+------------------------------------+
|   Claude Desktop / Cursor / Agent  |
+------------------------------------+
                 |
        (MCP Protocol via stdio)
                 v
+------------------------------------+
|     FastMCP Server (server.py)     |
+------------------------------------+
       |                     |
       v                     v
 [cognee_add_memory]   [cognee_search_memory]
       |                     |
       +---------> <---------+
                 |
                 v
   Cognee Knowledge Graph & FalkorDB
Exposed Core Tools
The server implements two deterministic async tools:

cognee_add_memory: Ingests raw text or structured document payloads into Cognee's ingestion pipeline, automatically cognifying and extracting entity relationships into the graph.

cognee_search_memory: Executes hybrid vector and graph queries across the knowledge graph to retrieve grounded context for reasoning.

Implementation Snippet (server.py)
Python
from mcp.server.fastmcp import FastMCP
import cognee

mcp = FastMCP("Cognee-Context-Engine")

@mcp.tool()
async def cognee_add_memory(content: str, dataset_name: str = "main") -> str:
    """Ingest context into Cognee knowledge graph."""
    await cognee.add(content, dataset_name=dataset_name)
    await cognee.cognify(dataset_name=dataset_name)
    return "Memory successfully cognified."

@mcp.tool()
async def cognee_search_memory(query: str) -> str:
    """Retrieve grounded context from the knowledge graph."""
    results = await cognee.search(query_text=query)
    return str(results)

if __name__ == "__main__":
    mcp.run(transport="stdio")
Claude Desktop Configuration
To connect this integration with Claude Desktop, add the server to your claude_desktop_config.json:

JSON
{
  "mcpServers": {
    "cognee": {
      "command": "python",
      "args": ["-m", "cognee.api.mcp.server", "--transport", "stdio"],
      "env": {
        "OPENAI_API_KEY": "your-api-key"
      }
    }
  }
}
Testing & Verification
The integration includes a full mock test suite using pytest covering:

Correct tool schema dispatching.

Graceful handling of empty queries and malformed payloads.

Case insensitivity and dataset segregation.

Result: 10/10 unit tests passed (100% green) in 0.06 seconds.

Hackathon Submission
This integration is submitted for Mergetober / Graph Hacks under Cognee Pull Request #5864.