AI Agent Starter with PydanticAI and MCP Server

Description

# AI Agent Starter with PydanticAI and MCP A base project using PydanticAI’s Agent API. Connects to any LLM provider (such as OpenAI, Groq, Azure OpenAI, etc.) and supports multiple MCP servers using a simple JSON config at the root of the project. Includes a minimal demo client to show tool calls and results. ## Features - **Multi-provider support:** Use any LLM provider with PydanticAI via `provider:model` syntax (e.g. `openai:gpt-4o`, `groq:llama-3.3-70b-versatile`) - **Multiple MCP servers:** Connect to several MCP servers, configured via `mcp_config.json` (modeled after the [Claude Desktop convention](https://modelcontextprotocol.io/quickstart/user)) - **Demo client included:** See basic tool call and agent response handling in action ## Prerequisites - [Docker](https://www.docker.com/) - [UV](https://github.com/astral-sh/uv) (alternative) ## Quickstart 1. **Clone the repo:** ```bash git clone https://github.com/ianrichard/mcp-llm-api-server.git cd mcp-llm-api-server ``` 1. **Copy example environment file** `cp .env.example .env` 1. **Edit `.env` with your provider and API key(s)** - Only the provider API key is needed for most providers. - See `.env.example` for the required fields for each provider. - This project uses [PydanticAI's provider:model syntax](https://ai.pydantic.dev/models/). - [Groq](https://groq.com/) is a simple provider to start with (no affiliation). 1. **[Optional] Set up multiple MCP servers** - Add/edit entries in `mcp_config.json` at the root. Follow the structure in the [MCP protocol quickstart](https://modelcontextprotocol.io/quickstart/user). 1. **Start the API server with Docker:** `docker-compose up --build` Once running, visit [http://localhost:8000](http://localhost:8000/) in your browser. ### Running with UV 1. Install uv `pip install --upgrade uv` 1. Install dependencies `uv sync` 1. Start the server `uv run -- uvicorn src.server.server:app --host 0.0.0.0 --port 8000` --- ### Azure OpenAI Configuration To use Azure OpenAI instead of regular OpenAI: - Fill out the additional Azure fields in your `.env` (see `.env.example`). - Make sure to specify your Azure endpoint, API key, and deployment/model names as described in the [PydanticAI docs](https://ai.pydantic.dev/models/). --- ## Using 3rd-Party or Custom MCP Servers If you want to test 3rd-party or custom MCP servers, you can use the [MCP Inspector](https://github.com/modelcontextprotocol/inspector). Simply run it like this in your terminal: `npx @modelcontextprotocol/inspector -- uvx mcp-server-fetch` **How it works:** - The text after the `--` (for example, `uvx mcp-server-fetch`) is _not_ part of the Inspector, but is the actual code or server command passed to it. - That code is what gets executed when you hit "Connect" in the Inspector UI. - You can use any server module or command—just change what's after the `--`. **Pay attention to your environment:** - Whatever you put after `--` is executed in the shell where you ran `npx @modelcontextprotocol/inspector`. - That means the required runtime (node, python, etc.) and any dependencies must be available there. - If you reference a custom or 3rd-party server in your `mcp_config.json`, make sure your local machine or Docker container (depending on where you run this project) has all the interpreters, runtimes, and environment set up appropriately.

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