MCP For Claude Marketplace
Cognee

Cognee MCP Connector for Claude

F

Build knowledge graphs from unstructured data — ingest text, extract entities and relationships, and search with graph-aware AI reasoning.

4 tools Official Updated Jun 28, 2026 Official Vinkius Partner
More Details Connect to Claude

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
knowledge-graphunstructured-datagraph-databaseai-reasoningdata-ingestionsemantic-search

4 tools expose this connector's capabilities to your AI agent.

cognee_add_data

After 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

cognee_cognify

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

cognee_get_insights

Useful for understanding relationships between topics, discovering hidden connections, and building comprehensive knowledge views. Retrieve structured entity relationships and insights from the knowledge graph

cognee_search

Search the knowledge graph using natural language. Returns context-aware answers using graph traversal and semantic search

See how to talk to your AI agent using Cognee.

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.'

Data ingested successfully! **[ingested]** | Dataset: default > Transformer models were introduced by Vaswani et al. in 2017... 📎 Data added to dataset 'default'. Run cognify to process into knowledge graph.

Process my data into a knowledge graph.

Knowledge graph processing started! **[cognify]** | Dataset: default > Extracted entities: Transformer, Vaswani, GPT, BERT, T5, self-attention > Relationships: introduced_by, foundation_for, uses_mechanism > Status: processing

What is the relationship between Transformers and GPT?

Graph-aware answer: > Transformers are the foundational architecture for GPT models. Introduced by Vaswani et al. in 2017, the Transformer architecture uses self-attention mechanisms. GPT (Generative Pre-trained Transformer) builds upon this architecture for autoregressive language modeling. Relationships found: - Transformer → foundation_for → GPT - Transformer → uses → self-attention - Transformer → introduced_by → Vaswani et al.

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.

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