> ## 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",
          "headers": {
            "Authorization": "Bearer {generated_token}"
          }
        }
      }
    }
    ```

    Replace `{generated_token}` with your Corridor API token. Prefer the `Authorization` header over putting the token in the URL query string. 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>