mcp-steampipe
스팀파이프 MCP
간단한 Steampipe MCP 서버입니다. AI 모델과 Steampipe 도구를 연결하는 다리 역할을 합니다.
필수 조건
Python 3.10+ 설치됨.
uv 설치됨(제가 제일 좋아하는 것) 및 mcp[cli]
스팀파이프가 설치되어 작동 중입니다.
필수 자격 증명(예: ~/.steampipe/config/github.spc의 토큰)을 사용하여 Steampipe 플러그인이 구성되었습니다(예: github).
MCP를 지원하는 LLM이 있나요? 저는 Claude Here를 사용합니다.
Node.js와 npx가 설치되었습니다(MCP Inspector에 필요하며 일부 MCP 서버를 실행하는 데도 필요함).
Related MCP server: steam-mcp
MCP 인터셉터 실행
이것은 MCP 서버가 예상대로 작동하는지 테스트하기 위한 훌륭한 도구입니다.
Interceptor
npx -y @modelcontextprotocol/inspector uv --directory . run steampipe_mcp_server.pyMCP Inspector UI(일반적으로 http://localhost:XXXX )가 있는 브라우저 창이 열립니다.
왼쪽 패널에서 "연결됨" 상태가 될 때까지 기다리세요.
도구 탭으로 이동합니다.
run_steampipe_query 도구와 해당 설명이 나열되어 있어야 합니다.
도구 이름을 클릭하세요.
"인수" JSON 입력 필드에 유효한 Steampipe 쿼리를 입력하세요.
지엑스피1
JSON 결과를 실행하고 봅니다.
도구 실행
아주 간단합니다. 인터셉터를 실행하고 도구가 디렉터리에서 제대로 작동하는지 확인하세요. 그런 다음 해당 LLM에 서버 구성을 추가하고 LLM에서 도구를 선택하세요.
문제 해결
인터셉터에서 도구를 찾을 수 없는 경우 @mcp.tool() 데코레이터에 문제가 있다는 의미입니다.
실행 오류 - Inspector의 "결과"와 터미널의 서버 로그(stderr)를 확인하세요. Steampipe가 실행되었나요? SQL 오류가 있었나요? 시간 초과가 발생했나요? JSON 구문 분석 오류가 있었나요? Python 스크립트를 적절히 수정하세요.
tail -f ~/Library/Logs/Claude/mcp.log
tail -f ~/Library/Logs/Claude/mcp-server-steampipe.log보안 위험 클로드는 이 POC에서 SQL 쿼리를 맹목적으로 실행하므로 구성된 자격 증명을 사용하여 Steampipe를 통해 임의의 SQL 쿼리를 생성하고 실행할 가능성이 있습니다.
Available Tools
1 toolrun_steampipe_queryB
Executes a SQL query using the Steampipe CLI and returns the results as a JSON string.
Args: query: The SQL query to execute via Steampipe (e.g., "select login from github_user limit 1"). Ensure the query is valid Steampipe SQL.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states that the tool executes a query and returns JSON results, but lacks critical details such as execution timeouts, error handling, authentication requirements, or rate limits. This leaves significant gaps in understanding how the tool behaves in practice.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and concise, with no wasted words. It starts with a clear purpose statement, followed by a labeled 'Args' section with a bullet point for the single parameter. Each sentence adds value, making it easy to scan and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (executing SQL queries with potential side effects) and the lack of annotations and output schema, the description is moderately complete. It covers the basic purpose and parameter semantics but misses behavioral details like error responses, performance considerations, or output structure, which are important for a query execution tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaningful context beyond the input schema, which has 0% description coverage. It explains that the 'query' parameter is 'The SQL query to execute via Steampipe' and provides an example, clarifying that it must be 'valid Steampipe SQL.' This compensates well for the schema's lack of detail, though it doesn't cover all potential edge cases.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Executes a SQL query using the Steampipe CLI and returns the results as a JSON string.' It specifies the verb ('executes'), resource ('SQL query'), and output format ('JSON string'). However, with no sibling tools mentioned, there's no explicit differentiation from alternatives, preventing a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides minimal usage guidance. It includes an example query but does not specify when to use this tool versus other methods (e.g., direct database access or other query tools). There is no mention of prerequisites, error conditions, or typical use cases beyond the basic example.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
1 tool update
v1.0.0- First observed
run_steampipe_query
TDQS
Scored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined and distinct by default.
A single tool inherently has perfect naming consistency, as there are no other tools to compare it against. The naming follows a clear verb_noun pattern (run_steampipe_query).
A single tool is too few for most practical purposes, as it severely limits the server's functionality and flexibility. While it might cover the basic query execution, it lacks any supporting operations like listing available tables, checking query status, or managing connections.
The server is severely incomplete for interacting with Steampipe. It only provides query execution, missing essential operations such as listing available plugins/tables, describing table schemas, managing connections, or handling query errors and metadata. This will cause significant agent failures in complex workflows.
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