MCP Search Server

# LLM Selector example This example shows using MCP's ModelPreferences type to select a model (LLM) based on speed, cost and intelligence priorities. https://github.com/user-attachments/assets/04257ae4-a628-4c25-ace2-6540620cbf8b --- ```plaintext ┌──────────┐ ┌─────────────────────┐ │ Selector │──┬──▶│ gpt-4o │ └──────────┘ │ └─────────────────────┘ │ ┌─────────────────────┐ ├──▶│ gpt-4o-mini │ │ └─────────────────────┘ │ ┌─────────────────────┐ ├──▶│ claude-3.5-sonnet │ │ └─────────────────────┘ │ ┌─────────────────────┐ └──▶│ claude-3-haiku │ └─────────────────────┘ ``` ## `1` App set up First, clone the repo and navigate to the mcp_model_selector example: ```bash git clone https://github.com/lastmile-ai/mcp-agent.git cd mcp-agent/examples/basic/mcp_model_selector ``` Install `uv` (if you don’t have it): ```bash pip install uv ``` Sync `mcp-agent` project dependencies: ```bash uv sync ``` Install requirements specific to this example: ```bash uv pip install -r requirements.txt ``` ## `2` Run locally Run your MCP Agent app: ```bash uv run main.py ``` ## `2.1` Run locally in Interactive mode Run your MCP Agent app: ```bash uv run interactive.py ```