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ramraj-patel

query-layer

by ramraj-patel

MCP Server for Query Layer API

Ein MCP-Server (Model Context Protocol), der eine Query-Layer-API für Metriken für KI-Assistenten bereitstellt. Erstellt mit dem offiziellen Python MCP SDK v2.

Schnellstart

# Prerequisites: Python 3.10+, uv
uv sync
uv run mcp dev src/server.py

Öffnen Sie den MCP Inspector unter der in der Konsole ausgegebenen URL. Sie sehen, dass das ping-Tool verfügbar ist.

Related MCP server: prometheus-mcp

Cursor-Integration

Die Datei .cursor/mcp.json ist vorkonfiguriert. Starten Sie Cursor neu. Danach steht der MCP-Server query-layer dem KI-Assistenten zur Verfügung.

Lokales Tracing mit Phoenix (optional)

# 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.py

Sehen Sie sich Traces unter http://localhost:6006 an — Phoenix rendert MCP-Tool-Aufrufe, Prompts und GenAI-Spans nativ.

Dokumentation

Dokument

Zweck

Plan

Problemstellung, Lösungsansatz, Umsetzungsplan

Review

Prüfung des Plans auf Produktionsreife

Design

MCP-Konzepte, Technologieentscheidungen, Designentscheidungen

Getting Started

Einrichtung, Installation, Start, Konfiguration, Fehlerbehebung

Observability

Logging, Tracing, OTLP, lokales Phoenix

Projektstruktur

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 guide

Lizenz

Apache 2.0 — siehe LICENSE.

A
license - permissive license
Not graded
quality - not tested
B
maintenance

Maintenance

Maintainers
Response time
Release cycle
Releases (12mo)
Commit activity

Resources

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