mcp-recognize-image

# mcp-recognize-image An MCP (Model Context Protocol) server that gives **text-only models the ability to "see" images**. It routes image-recognition requests through configurable multimodal (vision) LLM providers, with **priority-ordered automatic fallback**: if the top provider fails (auth / rate-limit / error / timeout), the next is tried. ## Install (end users) ```bash uvx mcp-recognize-image ``` Install [uv](https://docs.astral.sh/uv/) first if needed: ```bash curl -LsSf https://astral.sh/uv/install.sh | sh ``` Or with pip: ```bash pip install mcp-recognize-image ``` ## Configure Works out of the box with the bundled default providers (free vision endpoints pre-filled). You only need to set API keys for the providers you have: ```bash export GEMINI_API_KEY=... # free tier: https://aistudio.google.com export SILICONFLOW_API_KEY=... # free tier: https://cloud.siliconflow.cn export OPENROUTER_API_KEY=... # free models: https://openrouter.ai ``` ### Custom endpoint (zero config file) Point it at **any** OpenAI-compatible endpoint (OpenAI, Azure OpenAI, Ollama, LM Studio, DeepSeek, Moonshot, …) with three env vars — no config file needed. The API key lives in the env (never on disk): ```bash export VISION_BASE_URL="https://api.openai.com/v1" export VISION_API_KEY="sk-..." export VISION_MODEL="gpt-4o" # optional: VISION_ADAPTER (openai_compat|gemini|anthropic, default openai_compat) # optional: VISION_PRIORITY (default 0 = tried first) ``` With these set and no config file, **only** this custom provider is used. In an MCP client config, put the same vars in the `env` block. Config path resolution (only if you customize the provider list): 1. `VISION_MCP_CONFIG` env var (absolute path) 2. `~/.config/vision-mcp/config.toml` 3. bundled `config.example.toml` (fallback — used by default) To customize the provider list, copy the bundled example and edit (from a source checkout): ```bash mkdir -p ~/.config/vision-mcp cp src/vision_mcp/confi…