Cognee MCP Server for LlamaIndex 4 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Cognee as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Cognee. "
"You have 4 tools available."
),
)
response = await agent.run(
"What tools are available in Cognee?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Cognee MCP Server
Connect your AI agent to Cognee — the open-source knowledge graph platform that transforms unstructured data into structured, searchable knowledge.
LlamaIndex agents combine Cognee tool responses with indexed documents for comprehensive, grounded answers. Connect 4 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
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
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
The Cognee MCP Server exposes 4 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Cognee to LlamaIndex via MCP
Follow these steps to integrate the Cognee MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 4 tools from Cognee
Why Use LlamaIndex with the Cognee MCP Server
LlamaIndex provides unique advantages when paired with Cognee through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Cognee tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Cognee tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Cognee, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Cognee tools were called, what data was returned, and how it influenced the final answer
Cognee + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Cognee MCP Server delivers measurable value.
Hybrid search: combine Cognee real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Cognee to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Cognee for fresh data
Analytical workflows: chain Cognee queries with LlamaIndex's data connectors to build multi-source analytical reports
Cognee MCP Tools for LlamaIndex (4)
These 4 tools become available when you connect Cognee to LlamaIndex via MCP:
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
Example Prompts for Cognee in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Cognee immediately.
"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.'"
"Process my data into a knowledge graph."
"What is the relationship between Transformers and GPT?"
Troubleshooting Cognee MCP Server with LlamaIndex
Common issues when connecting Cognee to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpCognee + LlamaIndex FAQ
Common questions about integrating Cognee MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Cognee with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Cognee to LlamaIndex
Get your token, paste the configuration, and start using 4 tools in under 2 minutes. No API key management needed.