query-layer
Servidor MCP para Query Layer API
Un servidor MCP (Model Context Protocol) que expone una API de capa de consultas de métricas a asistentes de IA. Creado con el SDK oficial de MCP para Python v2.
Inicio rápido
# Prerequisites: Python 3.10+, uv
uv sync
uv run mcp dev src/server.pyAbra el MCP Inspector en la URL que se muestra en la consola. Verá la herramienta ping disponible.
Related MCP server: prometheus-mcp
Integración con Cursor
El archivo .cursor/mcp.json está preconfigurado. Reinicie Cursor y el servidor MCP query-layer estará disponible para el asistente de IA.
Trazado local con Phoenix (Opcional)
# Terminal 1: start Phoenix
uv run phoenix serve
# Terminal 2: run the server pointing at Phoenix
export PHOENIX_COLLECTOR_ENDPOINT=http://localhost:6006
uv run mcp dev src/server.pyVea las trazas en http://localhost:6006 — Phoenix renderiza de forma nativa las llamadas a herramientas MCP, los prompts y los spans de GenAI.
Documentación
Document | Purpose |
Descripción general, enfoque de la solución, plan de implementación | |
Revisión del plan de nivel de producción | |
Conceptos de MCP, opciones tecnológicas, decisiones de diseño | |
Puesta en marcha, instalación, ejecución, configuración y resolución de problemas | |
Registro, trazado, OTLP y Phoenix local |
Estructura del proyecto
src/
server.py # MCPServer + tools (entry point)
logging_config.py # Structured JSON logging to stderr
otel_config.py # OpenTelemetry exporter configuration
.cursor/
mcp.json # Cursor MCP server configuration
docs/
plan.md # Full implementation plan
review.md # Plan review
design.md # Design document
getting-started.md # Setup guide
observability.md # Observability guideLicencia
Apache 2.0 — consulte LICENSE.
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Maintenance
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