mcp-steampipe
Tubo de vapor MCP
Este es un servidor MCP de Steampipe simple. Actúa como puente entre tu modelo de IA y la herramienta Steampipe.
Prerrequisitos
Python 3.10+ instalado.
uv instalado (mi favorito) y mcp[cli]
Steampipe instalado y funcionando.
Complemento Steampipe configurado (por ejemplo, github) con las credenciales necesarias (por ejemplo, token en ~/.steampipe/config/github.spc).
¿Hay algún LLM que admita MCP? Estoy usando Claude Here.
Node.js y npx instalados (necesarios para el Inspector MCP y potencialmente para ejecutar algunos servidores MCP).
Related MCP server: steam-mcp
Ejecución del MCP Interceptor
Esta es una herramienta increíble para probar si su servidor MCP está funcionando como se espera.
Ejecutando el Interceptor
npx -y @modelcontextprotocol/inspector uv --directory . run steampipe_mcp_server.pyDebería abrirse una ventana del navegador con la interfaz de usuario del Inspector MCP (normalmente en http://localhost:XXXX ).
Espere el estado "Conectado" en el panel izquierdo.
Vaya a la pestaña Herramientas.
Debería ver la herramienta run_steampipe_query listada con su descripción.
Haga clic en el nombre de la herramienta.
En el campo de entrada JSON "Argumentos", ingrese una consulta Steampipe válida:
{
"query": "select name, fork_count from github_my_repository "
}ejecutar y ver los resultados json
Ejecución de la herramienta
Bastante sencillo. Simplemente ejecute el interceptor y asegúrese de que la herramienta funcione desde el directorio. Luego, agregue la configuración del servidor al LLM correspondiente y seleccione la herramienta desde el LLM.
Solución de problemas
Si la herramienta no se encuentra en el interceptor, eso significa que el decorador @mcp.tool() tiene algún problema.
Error de ejecución: Consulta el "Resultado" en el Inspector y los registros del servidor (stderr) en tu terminal. ¿Se ejecutó Steampipe? ¿Hubo un error de SQL? ¿Se agotó el tiempo de espera? ¿Se produjo un error de análisis de JSON? Ajusta el script de Python según corresponda.
tail -f ~/Library/Logs/Claude/mcp.log
tail -f ~/Library/Logs/Claude/mcp-server-steampipe.logRiesgo de seguridad Claude ejecuta ciegamente su consulta SQL en esta POC, por lo que existe la posibilidad de generar y ejecutar consultas SQL arbitrarias a través de Steampipe usando sus credenciales configuradas.
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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