Steampipe

Steampipe MCP

这是一个简单的 Steampipe MCP 服务，用作 AI 模型与 Steampipe 工具之间的桥梁。

前置条件

• 已安装 Python 3.10+。
• 已安装 uv（我的最爱）和 mcp[cli]。
• 已安装并正常运行 Steampipe。
• 已配置 Steampipe 插件（例如 github），并提供必要的凭据（例如，~/.steampipe/config/github.spc 中的 token）。
• 支持 MCP 的任意大模型。我在这里使用 Claude。
• 已安装 Node.js 和 npx（MCP Inspector 和运行某些 MCP 服务时可能需要）。

运行 MCP Interceptor

这是一个很棒的工具，可用于测试 MCP 服务是否按预期工作。

• 运行 Interceptor npx -y @modelcontextprotocol/inspector uv --directory . run steampipe_mcp_server.py
• A browser window should open with the MCP Inspector UI (usually at http://localhost:XXXX).
• Wait for the "Connected" status on the left panel.
• Go to the Tools tab.
• You should see the run_steampipe_query tool listed with its description.
• Click on the tool name.
• In the "Arguments" JSON input field, enter a valid Steampipe query:

{
  "query": "select name, fork_count from github_my_repository "
}

• execute and view the json results

Running the tool

Pretty straightforward. Just run the interceptor and make sure the tool is working from the directory. Then add the server configuration to the respective LLM and select the tool from the LLM.

TroubleShooting

• If the tool is not found in the interceptor then that means @mcp.tool() decorator has some issue.
• Execution error - Look at the "Result" in the Inspector and the server logs (stderr) in your terminal. Did Steampipe run? Was there a SQL error? A timeout? A JSON parsing error? Adjust the Python script accordingly.

tail -f ~/Library/Logs/Claude/mcp.log
tail -f ~/Library/Logs/Claude/mcp-server-steampipe.log

Security Risk Claude blindly executes your sql query in this POC so there is possibility to generate and execute arbitary SQL Queries via Steampipe using your configured credentials.