What is Mcp Steampipe

mcp-steampipe is a simple MCP server written in Python that serves as a bridge between AI models and the Steampipe tool, facilitating seamless interaction and data retrieval.

Use cases

Use cases for mcp-steampipe include querying GitHub repositories, retrieving data from cloud services, and integrating AI models with data sources for enhanced decision-making.

How to use

To use mcp-steampipe, ensure you have Python 3.10+, uv, and Steampipe installed. Run the MCP Interceptor using the command ‘npx -y @modelcontextprotocol/inspector uv --directory . run steampipe_mcp_server.py’ and follow the instructions to execute queries through the MCP Inspector UI.

Key features

Key features of mcp-steampipe include compatibility with any LLM supporting MCP, the ability to run Steampipe queries, and an intuitive MCP Inspector for testing and debugging.

Where to use

mcp-steampipe can be used in various fields such as data analysis, AI model integration, and software development where interaction with databases and APIs is required.

Steampipe MCP

This is a simple steampipe MCP server. This acts as a bridge between your AI model and Steampipe tool.

Pre-requisites

• Python 3.10+ installed.
• uv installed (my fav) and mcp[cli]
• Steampipe installed and working.
• Steampipe plugin configured (e.g., github) with necessary credentials (e.g., token in ~/.steampipe/config/github.spc).
• Any LLM supporting MCP. I am using Claude Here.
• Node.js and npx installed (required for the MCP Inspector and potentially for running some MCP servers).

Running MCP Interceptor

This is an awesome tool for testing your if your MCP server is working as expected

• Running the 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.