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Turbot Guardrails MCP Server

@turbot

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基本信息

分类

其他

许可证

Apache-2.0

传输方式

stdio

作者

turbot

关于 Turbot Guardrails MCP Server

Enable AI assistants to explore and query your Turbot Guardrails data!

#mcp#turbot-guardrails#mcp-server

Turbot Guardrails MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "guardrails-mcp": {
      "command": "npx",
      "args": [
        "@modelcontextprotocol/inspector",
        "node",
        "dist/index.js"
      ]
    }
  }
}

Turbot Guardrails MCP Server 工具

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当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

点击上方按钮,在临时沙箱中运行此服务器并实时获取工具列表。

概览

What is Turbot Guardrails Model Context Protocol (MCP) Server?

This MCP server connects AI assistants such as Claude to a Turbot Guardrails environment, enabling natural language exploration, analysis, and automation across cloud resources. It is designed for cloud engineers and governance teams who want to query, control, and monitor their cloud estate through conversational interfaces.

How to use Turbot Guardrails Model Context Protocol (MCP) Server?

Install via npm (npx -y @turbot/guardrails-mcp) and configure using either a Turbot CLI profile (set TURBOT_PROFILE) or direct environment variables (TURBOT_WORKSPACE, TURBOT_ACCESS_KEY, TURBOT_SECRET_KEY). Legacy v0.1.x variable names are also accepted. Add the configuration to your AI assistant's MCP settings file (e.g., claude_desktop_config.json for Claude Desktop) and restart the assistant.

Key features of Turbot Guardrails Model Context Protocol (MCP) Server

  • Run read-only GraphQL queries against Guardrails data.
  • List and filter resources, controls, and policy types.
  • Execute controls by ID for compliance remediation.
  • Introspect GraphQL schemas of runnable types.
  • Render Nunjucks templates for dynamic configurations.

Use cases of Turbot Guardrails Model Context Protocol (MCP) Server

  • Query cloud resources (e.g., S3 buckets, EC2 instances) using natural language.
  • Generate compliance and security reports on non-compliant resources.
  • Explore available policy and control types by category or title.
  • Run controls on specific resources directly from the AI assistant.
  • Process and apply Nunjucks templates for dynamic policy or configuration generation.

常见问题

1

How do I authenticate to Guardrails?

You can authenticate either by referencing a Turbot CLI profile with `TURBOT_PROFILE` or by setting the three direct credential variables: `TURBOT_WORKSPACE`, `TURBOT_ACCESS_KEY`, and `TURBOT_SECRET_KEY`. Legacy v0.1.x names (e.g., `TURBOT_GRAPHQL_ENDPOINT`) are still accepted.

2

What are the prerequisites?

Node.js version 20 or higher, a valid Guardrails API access key with appropriate permissions, and the endpoint URL for your Guardrails workspace.

3

Where does the server look for credentials by default?

If using a CLI profile, the server reads `~/.config/turbot/credentials.yml` by default. This path can be overridden with the `TURBOT_CLI_CREDENTIALS_PATH` environment variable.

4

What should I do if I see authentication errors?

Verify that your API key is correct and has the necessary permissions. Ensure the profile name matches an entry in your credentials file and that the file includes `workspace`, `accessKey`, and `secretKey`. Credential values are redacted from error messages returned to your AI assistant.

5

How can I test the server locally?

Use the MCP Inspector by running `npm run build` then `npx @modelcontextprotocol/inspector node dist/index.js`. This launches an interactive test interface for the server.

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