How to Use the Cognee MCP in Mastra AI

Automate complex knowledge workflows. Connect Cognee to Mastra AI to trigger graph-based decisions in your agents.

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…and any MCP-compatible client

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Connect Cognee MCP to Mastra AI

Create your Vinkius account to connect Cognee to Mastra AI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Cognee with Mastra AI

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Build graph workflows with Mastra AI

Use `cognee_add_data` inside your agent logic to feed new info into the graph. Mastra AI handles the retries if the network blips, keeping your ingestion pipeline steady. This keeps your knowledge base updated without manual intervention. Just define the trigger, and the agent sends the data to the server.

Automate entity extraction for agents

Trigger `cognee_cognify` as part of a multi-step workflow. Your agents now have a structured map to reference when making decisions. This creates an automated loop where the agent constanty refines its own understanding. It stops the agent from hallucinating by grounding every response in the graph.

Retrieve insights via Mastra AI tools

Call `cognee_get_insights` to let your agents reason over complex relationships. You can branch your logic based on what the graph reveals about the connections. Use `cognee_search` to ground agent answers in your specific data. It turns generic agents into subject matter experts for your specific business domain.

Setup guide

Set up Cognee MCP in Mastra AI

Prerequisites

Node.js 18+ and a TypeScript project
@mastra/mcp + @mastra/core packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).
2

Configure the MCPClient
Create an MCPClient with your Vinkius endpoint as a URL object. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and inject tools
Call mcpClient.listTools() and spread the result into your agent's tools object. All Cognee tools become native Mastra tools.
4

Run with any model
Swap openai("gpt-4o") for any AI SDK-compatible provider. Call agent.generate() and the agent routes tool calls through MCP automatically.

agent.ts

import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

const mcpClient = new MCPClient({
  id: "cognee-mcp-client",
  servers: {
    "cognee-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "Cognee Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to Cognee tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent Cognee transactions"
);
console.log(result.text);

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Cognee. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

Why Choose Vinkius

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Common questions about Cognee MCP in Mastra AI

Add the server URL to your agent configuration. Mastra AI detects the available tools, allowing you to call them directly within your agent's task steps.

Yes, you can write workflow logic that checks if a graph update is needed before firing the tool. This keeps your API usage efficient.

You can set tool approval requirements on the server tools. The agent will pause and wait for you to confirm the action before the graph changes.

The server handles all graph traversal and semantic search. Your agent just sends the natural language query and receives the structured answer.

Data is encrypted at rest and in transit within our isolated server environment. We only store the vector embeddings and entity relationships, keeping your raw source files isolated from any public model training.

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