Setup MCP Server

Set up the Future AGI MCP Server to interact with the platform via natural language from Claude, Cursor, or VS Code using Model Context Protocol.

What is it?

The Future AGI MCP Server lets you interact with the entire Future AGI platform through natural language, directly from your AI coding environment. Instead of switching between the dashboard and your editor, you can run evaluations, upload datasets, generate synthetic data, and apply protection rules just by describing what you want in tools like Claude, Cursor, or VS Code. It’s built on the Model Context Protocol — a standard that connects AI models to external tools and services.

How it works

Install

Add the MCP server to your IDE using the config below.

Authorize

OAuth login opens in your browser automatically — no API keys needed.

Start using

Ask your AI assistant about your evaluations, traces, datasets, and more.

Connect Your IDE

All you need is this URL — authentication happens automatically via OAuth 2.0:

https://api.futureagi.com/mcp
One-click install for Cursor
Opens Cursor and adds FutureAGI as an MCP server automatically.
Or add manually:
~/.cursor/mcp.json
{
  "mcpServers": {
    "futureagi": {
      "url": "https://api.futureagi.com/mcp"
    }
  }
}
Run this command in your terminal
This registers FutureAGI as an MCP server in Claude Code.
Or add manually:
Run in terminal
claude mcp add futureagi --transport http https://api.futureagi.com/mcp
One-click install for VS Code
Opens VS Code and adds FutureAGI as an MCP server automatically.
Or add manually:
.vscode/settings.json
{
  "mcp.servers": {
    "futureagi": {
      "type": "http",
      "url": "https://api.futureagi.com/mcp"
    }
  }
}
Add to your config file:
claude_desktop_config.json
{
  "mcpServers": {
    "futureagi": {
      "url": "https://api.futureagi.com/mcp"
    }
  }
}
Add to your config file:
~/.codeium/windsurf/mcp_config.json
{
  "mcpServers": {
    "futureagi": {
      "serverUrl": "https://api.futureagi.com/mcp"
    }
  }
}

What You Can Do

With Future AGI’s MCP Server, you can use natural language to:

  • Run automatic evaluations — Evaluate batch and single inputs on various evaluation metrics, both on local datapoints and large datasets
  • Prototype and observe your agents — Add observability, evaluations while prototyping and deploying agents into production
  • Manage datasets — Upload, evaluate, download datasets and find insights
  • Add protection rules — Apply toxicity detection, prompt injection protection, and other guardrails automatically
  • Generate synthetic data — Describe your dataset and objective to generate synthetic data

Check out our blog post on futureagi-mcp-server for detailed use cases.

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