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ramraj-patel

query-layer

by ramraj-patel
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Python

指标查询层 API 的 MCP 服务器

一个 MCP（Model Context Protocol）服务器，向 AI 助手暴露指标查询层 API。使用官方 Python MCP SDK v2 构建。

快速入门

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

在控制台打印的 URL 打开 MCP Inspector。你将看到 ping 工具可用。

Related MCP server: prometheus-mcp

与 Cursor 集成

.cursor/mcp.json 文件已预先配置。重启 Cursor 后，query-layer MCP 服务器即可供 AI 助手使用。

使用 Phoenix 进行本地追踪（可选）

# 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

在 http://localhost:6006 查看追踪信息 — Phoenix 原生渲染 MCP 工具调用、提示词和 GenAI spans。

文档

文档

用途

计划

问题陈述、解决方案、实施计划

审查

生产级计划审查

设计

MCP 概念、技术选型、设计决策

快速入门

设置、安装、运行、配置、故障排查

可观测性

日志、追踪、OTLP、本地 Phoenix

项目结构

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

许可证

Apache 2.0 — 请参阅 LICENSE。

This server cannot be installed

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

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