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The Relevance AI MCP server gives any MCP-compatible AI client direct access to your agents, tools, and knowledge. Connect from the AI tools you already use and start building. The MCP server is available at: 
https://mcp.relevanceai.com/
For Claude Code, we recommend using the Relevance AI plugin instead of a manual MCP connection — it includes built-in skills and context that make the experience significantly better.
This page is about using Relevance AI from external AI clients. If you want to connect an external MCP server to a Relevance AI agent, see MCP Client.

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Supported clients

  1. Open Claude Desktop
  2. Go to Settings → Connectors
  3. Click Add connector
  4. Enter the server URL: https://mcp.relevanceai.com/
  5. Follow the authentication prompts to connect your Relevance AI project
  1. Navigate to the Connectors page in Claude.ai
  2. Click Add connector
  3. Enter the server URL: https://mcp.relevanceai.com/
  4. Follow the authentication prompts
ChatGPT supports MCP servers through Developer Mode, available on Pro, Team, Enterprise, and Edu plans.
  1. Open ChatGPT Settings
  2. Go to Connectors → Advanced → Developer Mode
  3. Click Add connector
  4. Enter the server URL: https://mcp.relevanceai.com/
  5. Set Authentication to OAuth and follow the login flow
Once connected, the Relevance AI tools will be available in both Chat and Deep Research modes.
  1. Open Cursor Settings
  2. Navigate to the MCP tab
  3. Click Add new MCP server
  4. Use the following configuration in your mcp.json:
{
  "mcpServers": {
    "relevance-ai": {
      "command": "npx",
      "args": ["-y", "mcp-remote", "https://mcp.relevanceai.com/"]
    }
  }
}
Add the following to your VS Code settings (.vscode/mcp.json):
{
  "mcpServers": {
    "relevance-ai": {
      "command": "npx",
      "args": ["-y", "mcp-remote", "https://mcp.relevanceai.com/"]
    }
  }
}
Add the following to your Windsurf MCP configuration:
{
  "mcpServers": {
    "relevance-ai": {
      "command": "npx",
      "args": ["-y", "mcp-remote", "https://mcp.relevanceai.com/"]
    }
  }
}
Run the following command:
codex --mcp-server https://mcp.relevanceai.com/
You can also set the MCP server via an environment variable:
export CODEX_MCP_SERVER=https://mcp.relevanceai.com/
Add the following to your Zed settings (settings.json):
{
  "language_models": {
    "mcp": {
      "servers": {
        "relevance-ai": {
          "command": "npx",
          "args": ["-y", "mcp-remote", "https://mcp.relevanceai.com/"]
        }
      }
    }
  }
}
Add the following MCP configuration in your v0 project settings:
{
  "mcpServers": {
    "relevance-ai": {
      "command": "npx",
      "args": ["-y", "mcp-remote", "https://mcp.relevanceai.com/"]
    }
  }
}
If you prefer to add the MCP server directly without the plugin:
claude mcp add relevance-prod --transport http https://mcp.relevanceai.com/
Once added, run /mcp from within Claude Code. You will see the new MCP server in the list. Select it to connect and follow the authentication steps.
For any MCP-compatible client, use the server URL:
https://mcp.relevanceai.com/
If your client requires an npx command, use:
npx -y mcp-remote https://mcp.relevanceai.com/

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Authentication

When you first connect, you will be prompted to authenticate with your Relevance AI account. Authentication is per project — you will be connected to a specific Relevance AI project after logging in.

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Working with multiple projects

If you work across multiple Relevance AI projects, add a separate MCP server entry for each: 
{
  "mcpServers": {
    "relevance-project-1": {
      "command": "npx",
      "args": ["-y", "mcp-remote", "https://mcp.relevanceai.com/"]
    },
    "relevance-project-2": {
      "command": "npx",
      "args": ["-y", "mcp-remote", "https://mcp.relevanceai.com/"]
    }
  }
}
Each entry authenticates independently against its own project, so you can access tools and agents across all your projects without logging out and back in.
Alternatively, you can use a single connection and log out / log back in to switch projects — but the multi-connection approach above is preferred for convenience.

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Add agent skills

The MCP server gives your AI assistant the ability to call Relevance AI tools, but it doesn’t know how to use them well. For better results, pair it with the agent skills repository — a local reference that teaches your assistant how to work with agents, tools, workforces, knowledge, and more.

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Handling long-running agent executions

When triggering agents via MCP, you have two execution modes available depending on how long your agent takes to complete.

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Execution modes

The relevance_trigger_agent tool waits for the agent to finish and returns the result directly. It has a 120-second timeout, so use it for agents that complete quickly — single-step agents with minimal tool usage and no workforce nodes.
If you encounter timeout errors with relevance_trigger_agent, switch to the async pattern. Agents with workforce nodes should always use async execution.

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Async execution workflow

1

Trigger the agent

Call relevance_trigger_agent_async with your agent parameters. This returns immediately with a conversation_id.
2

Poll for results

Use relevance_poll_agent_result with the conversation_id to check the execution status. Poll every 3-5 seconds until the status is complete or failed.
3

Retrieve the results

The status will be working or in progress while the agent is executing, complete when results are available, or failed if an error occurred. Once complete, the poll response contains the agent’s output.

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Troubleshooting

  • Make sure you have an active Relevance AI account
  • Check that you have access to the project you are trying to connect to
  • Try removing and re-adding the MCP server connection
  • Verify that you have tools configured in your Relevance AI project
  • Check that you are authenticated to the correct project
  • Try disconnecting and reconnecting the MCP server
  • Ensure you have a stable internet connection
  • Check that https://mcp.relevanceai.com/ is accessible from your network
  • Try removing and re-adding the MCP server connection in your client
  • Try clearing the auth cache: rm -rf ~/.mcp-auth
Timeout errors from relevance_trigger_agent mean your agent exceeded the 120-second synchronous limit. Switch to relevance_trigger_agent_async and relevance_poll_agent_result instead. See handling long-running agent executions for the full workflow. Agents with workforce nodes, multi-step chains, or complex workflows should always use the async pattern.

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Frequently asked questions (FAQs)

The Model Context Protocol (MCP) is an open standard that allows AI clients to connect to external tools and data sources. It provides a standardized way for AI assistants to access your Relevance AI workspace.
The MCP server itself is free. You will be billed for any Relevance AI usage (agent runs, tool executions, etc.) according to your plan.
Yes. You can connect to the Relevance AI MCP server from as many clients as you like simultaneously. Each client authenticates independently.
Authentication tokens may expire after a period of inactivity. If you are prompted to re-authenticate, simply follow the login flow again.
The MCP server exposes the tools and agents available in the project you authenticated against. To control access, organize your tools across different projects and authenticate each connection to the appropriate project.