Cognee MCP Server
Build knowledge graphs from unstructured data — ingest text, extract entities and relationships, and search with graph-aware AI reasoning.
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What is the Cognee MCP Server?
The Cognee MCP Server gives AI agents like Claude, ChatGPT, and Cursor direct access to Cognee via 4 tools. Build knowledge graphs from unstructured data — ingest text, extract entities and relationships, and search with graph-aware AI reasoning. Powered by the Vinkius - no API keys, no infrastructure, connect in under 2 minutes.
Built-in capabilities (4)
Tools for your AI Agents to operate Cognee
Ask your AI agent "Add this research data to my knowledge base: 'Transformer models were introduced by Vaswani et al. in 2017 in the paper Attention Is All You Need. They use self-attention mechanisms and have become the foundation for models like GPT, BERT, and T5.'" and get the answer without opening a single dashboard. With 4 tools connected to real Cognee data, your agents reason over live information, cross-reference it with other MCP servers, and deliver insights you would spend hours assembling manually.
Works with Claude, ChatGPT, Cursor, and any MCP-compatible client. Powered by the Vinkius - your credentials never touch the AI model, every request is auditable. Connect in under two minutes.
Why teams choose Vinkius
One subscription gives you access to thousands of MCP servers - and you can deploy your own to the Vinkius Edge. Your AI agents only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure and security, zero maintenance.
Build your own MCP Server with our secure development framework →Vinkius works with every AI agent you already use
…and any MCP-compatible client
Cognee MCP Server capabilities
4 toolsAfter ingestion, use the cognify tool to process the data into a structured knowledge graph with entities and relationships. Ingest text or documents into the Cognee knowledge base. This is the first step before building a knowledge graph
This step extracts entities, identifies relationships, generates embeddings, and creates the graph structure needed for intelligent search. Process ingested data into a structured knowledge graph. Extracts entities, relationships, and builds a searchable graph structure
Useful for understanding relationships between topics, discovering hidden connections, and building comprehensive knowledge views. Retrieve structured entity relationships and insights from the knowledge graph
Search the knowledge graph using natural language. Returns context-aware answers using graph traversal and semantic search
What the Cognee MCP Server unlocks
Connect your AI agent to Cognee — the open-source knowledge graph platform that transforms unstructured data into structured, searchable knowledge.
What you can do
- Add Data — Ingest raw text, documents, or structured data into named datasets. Cognee processes and stores the data for subsequent graph construction
- Cognify — Transform ingested data into a structured knowledge graph by automatically extracting entities, relationships, and semantic connections
- Search Knowledge — Query the knowledge graph using four retrieval strategies: graph-aware completion (LLM + graph traversal), summaries, structured insights, or raw vector similarity
- Get Insights — Retrieve structured entity relationships showing how concepts connect across your knowledge base
How it works
1. Subscribe to this server
2. Enter your Cognee API key
3. Ingest data → Cognify → Search with graph-aware reasoning
Why Cognee over standard RAG?
- Relationship-aware — understands HOW facts connect, not just what they say
- Graph + Vector hybrid — combines graph traversal with semantic search for superior recall
- Temporal awareness — tracks when facts were added and reason over time-based connections
Frequently asked questions about the Cognee MCP Server
How is Cognee different from standard RAG?
Standard RAG splits documents into chunks and finds similar text using vector search — but it loses the relationships between facts. Cognee builds a knowledge graph that preserves entity relationships, temporal connections, and hierarchical structures. When you search, Cognee uses graph traversal combined with vector similarity and LLM reasoning, resulting in more accurate, context-aware answers that understand HOW facts relate to each other.
What search types are available?
Cognee supports four retrieval strategies: GRAPH_COMPLETION (default — combines vector search + graph traversal + LLM reasoning for context-aware answers), SUMMARIES (fast hierarchical overview search), INSIGHTS (structured entity relationships), and CHUNKS (pure vector similarity for raw text passages). Each strategy optimizes for different use cases.
Is Cognee open-source?
Yes! Cognee is fully open-source under the Apache 2.0 license. You can self-host the entire platform including the knowledge graph engine, vector database, and API server. A managed cloud version with API keys is also available for teams that prefer not to manage infrastructure.
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Give your AI agents the power of Cognee MCP Server
Production-grade Cognee MCP Server. Verified, monitored, and maintained by Vinkius. Ready for your AI agents — connect and start using immediately.