> ## Documentation Index
> Fetch the complete documentation index at: https://docs.corridor.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Custom MCP

> Set up Corridor as an MCP server for any AI coding tool that supports the Model Context Protocol.

Corridor can integrate with any MCP-compatible tool. Corridor's MCP server is standards-compliant, so any tool that can call an MCP endpoint can leverage Corridor's security guardrails.

## Prerequisites

* An AI coding tool that supports MCP servers
* A Corridor account with a team created

## Setup

<Steps>
  <Step title="Generate a Corridor API token">
    Generate a Corridor API token in your [Corridor settings](https://app.corridor.dev/settings).
  </Step>

  <Step title="Configure your MCP client">
    Configure your MCP client with the following:

    ```json theme={null}
    {
      "mcpServers": {
        "corridor": {
          "transport": "http",
          "url": "https://app.corridor.dev/api/mcp?token={generated_token}"
        }
      }
    }
    ```

    Replace `{generated_token}` with your Corridor API token. The exact configuration format may vary depending on your tool—refer to your tool's documentation for how to add an HTTP MCP server.
  </Step>
</Steps>

Once configured, your AI tool will consult Corridor's guardrails during code generation, providing security feedback automatically.

## Next steps

<CardGroup cols={2}>
  <Card title="Guardrails" icon="shield-check" href="/features/guardrails">
    Learn how guardrails protect your code
  </Card>

  <Card title="Corridor MCP" icon="plug" href="/features/corridor-mcp">
    Explore Corridor's MCP tools
  </Card>
</CardGroup>