query-layer
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.pyÖffnen 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.pySehen Sie sich Traces unter http://localhost:6006 an — Phoenix rendert MCP-Tool-Aufrufe, Prompts und GenAI-Spans nativ.
Dokumentation
Dokument | Zweck |
Problemstellung, Lösungsansatz, Umsetzungsplan | |
Prüfung des Plans auf Produktionsreife | |
MCP-Konzepte, Technologieentscheidungen, Designentscheidungen | |
Einrichtung, Installation, Start, Konfiguration, Fehlerbehebung | |
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 guideLizenz
Apache 2.0 — siehe LICENSE.
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Maintenance
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