MCP server for dynamic AI model switching in ai-lib ecosystem
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# MCP Model Switcher for ai-lib Ecosystem [](https://www.python.org/downloads/) [](LICENSE) MCP (Model Context Protocol) server that enables agents to dynamically switch AI models from the [ai-lib ecosystem](https://github.com/hiddenpath/ai-lib-python). ## Features - **Protocol-Driven**: All model configurations loaded from ai-protocol manifests (ARCH-001) - **Multi-Provider Support**: Switch between OpenAI, Anthropic, Google, DeepSeek, and more - **Runtime-Agnostic**: Uses ai-lib-python SDK for unified model interaction - **MCP-Compliant**: Implements standard MCP tools over stdio transport - **Capability Discovery**: Query available models and their capabilities ## Quick Start ### Installation ```bash # Clone the repository git clone https://github.com/yourorg/ai-mcp-model-switcher.git cd ai-mcp-model-switcher # Install dependencies pip install -e . ``` ### Environment Setup Set up your API keys: ```bash export OPENAI_API_KEY="sk-..." export ANTHROPIC_API_KEY="sk-ant-..." export GOOGLE_API_KEY="..." ``` Recommended provider key mapping: | Provider | Environment Variable | |----------|----------------------| | openai | `OPENAI_API_KEY` | | anthropic | `ANTHROPIC_API_KEY` | | google | `GOOGLE_API_KEY` or `GEMINI_API_KEY` | | deepseek | `DEEPSEEK_API_KEY` | | cohere | `COHERE_API_KEY` | | mistral | `MISTRAL_API_KEY` | Security note: - Prefer environment variables over passing `api_key` in tool arguments. - The server redacts sensitive fields in logs, but passing secrets in arguments still increases exposure risk in client traces. ### Configuration Add to your MCP client configuration (e.g., Cursor, Claude Desktop): ```json { "mcpServers": { "ai-model-switcher": { "command": "python", "args": ["-m", "ai_mcp_model_switcher.server"], "env": { "AI_PROTOCOL_PATH": "/path/to/ai-…
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