Overview

The Topoteretes Cognee MCP Server is a specialized Model Context Protocol (MCP) service that bridges the powerful AI memory engine cognee with external AI assistants such as Claude. By exposing cognee’s graph‑based knowledge creation and retrieval capabilities through MCP, the server enables developers to inject persistent, context‑aware reasoning into conversational agents without having to build a custom backend from scratch.

At its core, the server offers a single, versatile tool: Cognify_and_search. This endpoint accepts raw text to construct a knowledge graph, then performs a semantic search over that graph using a query string. The result is a set of edges (relationships) that the assistant can use to answer questions, trace reasoning chains, or suggest related topics. Optional parameters let users plug in custom Pydantic graph models, giving fine‑grained control over schema and validation. This tight coupling of knowledge creation and retrieval is what makes the server particularly valuable for developers who need dynamic, evolving context rather than static prompt templates.

Key capabilities include:

• Dynamic graph construction – Convert arbitrary text into a structured knowledge graph on the fly.
• Semantic search – Retrieve relevant edges based on natural‑language queries, leveraging underlying vector databases.
• Custom model support – Override default graph schemas with user‑defined Pydantic models for domain‑specific structures.
• Multi‑provider configuration – Swap out graph, vector, and database backends (e.g., NetworkX, Lancedb, SQLite) via environment variables, enabling flexible deployment across environments.

Typical use cases span several domains. In customer support, an AI agent can ingest product documentation and instantly answer user queries by traversing the generated graph. In research assistants, the server can process literature abstracts to build a citation network and surface related works during conversations. For internal knowledge bases, teams can feed meeting notes or technical docs into the server and let assistants retrieve actionable insights on demand.

Integration is straightforward for MCP‑aware clients. A developer configures the server path and environment variables once, then invokes Cognify_and_search from within the assistant’s workflow. The assistant receives a structured response, which it can embed directly into replies or use to trigger further actions. Because the server handles all heavy lifting—graph construction, vector indexing, and search—the client code remains clean and focused on higher‑level conversational logic.

In summary, the Topoteretes Cognee MCP Server transforms raw text into a searchable knowledge graph and exposes this functionality to AI assistants through a single, well‑defined MCP tool. It offers developers an out‑of‑the‑box solution for adding persistent, contextually rich memory to conversational agents, with the flexibility to tailor graph schemas and storage backends to their specific needs.