本地 MCP 橋樑：把多個 API key + endpoint 收編為統一的模型能力池，給 Agent 透過 MCP 調用
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# ai-mcp-server **Languages:** English | [繁體中文](README.zh-TW.md) | [简体中文](README.zh-CN.md) Local MCP bridge: register multiple (api_key, base_url) pairs once, and let your Agent automatically discover and route to any model with the right capability (chat, vision, reasoning, embedding, image_gen, tts, stt, rerank). Three entry points: - **`ai-mcp`** — CLI (manage endpoints, query models, trigger probes, init wizard) - **`ai-mcp-server`** — MCP stdio server, launched by Claude Desktop / Cursor / Cline / Trae - **`ai-mcp ui`** — local Web management dashboard (FastAPI + Jinja2, bound to 127.0.0.1) ## Install ### Option 1: uv (recommended) ```bash uv tool install ai-mcp-server ``` ### Option 2: Homebrew ```bash brew install brianMacao/tap/ai-mcp-server ``` ### Option 3: npm / npx ```bash npx ai-mcp-server # auto-installs uv + Python package ``` ### Option 4: pip ```bash pip install ai-mcp-server ``` ## Quickstart ```bash # Interactive first-run wizard ai-mcp init # Or step by step: ai-mcp endpoint add --name openrouter --base-url https://openrouter.ai/api/v1 --key sk-... ai-mcp endpoint probe openrouter ai-mcp model list --capability vision # Start the Web UI ai-mcp ui # → http://127.0.0.1:8765/ # Start the MCP server (for Claude Desktop, Cursor, etc.) ai-mcp-server ``` ## MCP Tools `ai-mcp-server` exposes 6 MCP tools: - `usage_guide` — dynamic inventory, capability distribution, and routing guidance. - `list_models` — filter models by capability, context length, endpoint, and probe state. - `invoke_model` — pass through chat / embedding / image_gen / tts / stt / rerank calls; TTS audio is returned as `audio_base64` inside the JSON body. - `model_performance` — inspect recent per-model call counts, success rate, and latency. - `refresh_endpoint` — refresh model lists and enqueue asynchronous capability probes. - `add_models` — manually register models for endpoints without `/v1/models`, or let an Agent register user-confirmed model feature…
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