AI Agent Starter with PydanticAI and MCP Server

@ianrichard

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Basic information

About AI Agent Starter with PydanticAI and MCP Server

No overview available yet

AI Agent Starter with PydanticAI and MCP Server config

No standard config provided

This server doesn't expose a parseable MCP config block in its README. See the repository for install instructions.

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AI Agent Starter with PydanticAI and MCP Server tools

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Overview

What is AI Agent Starter with PydanticAI and MCP?

A base project using PydanticAI’s Agent API that connects to any LLM provider (e.g., OpenAI, Groq, Azure OpenAI) and supports multiple MCP servers via a simple JSON configuration. It includes a minimal demo client to show tool calls and results. Aimed at developers building AI agents with flexible backends.

How to use AI Agent Starter with PydanticAI and MCP?

Clone the repository, copy .env.example to .env, edit with your provider and API key, optionally configure MCP servers in mcp_config.json, then start the API server with Docker (docker-compose up --build) or with UV (uv sync then uv run -- uvicorn src.server.server:app). Access the running server at http://localhost:8000.

Key features of AI Agent Starter with PydanticAI and MCP

Multi-provider LLM support via provider:model syntax
Connects to multiple MCP servers configured in mcp_config.json
Demo client included for testing tool calls
Deployable with Docker or UV
Azure OpenAI configuration supported

Use cases of AI Agent Starter with PydanticAI and MCP

Build AI agents that use different LLM providers for the same tool chain
Integrate several MCP servers (weather, filesystem, etc.) into one agent
Prototype agent workflows locally with a built-in demo client

Frequently asked questions

What prerequisites are needed to run the project?

Docker or UV (Python package manager) must be installed. The project also requires API keys for the chosen LLM provider.

How do I configure multiple MCP servers?

Edit the `mcp_config.json` file at the project root, following the structure from the MCP protocol quickstart. Each entry specifies a server command and its arguments.

Can I use Azure OpenAI instead of standard OpenAI?

Yes. Fill out the additional Azure fields in your `.env` file as described in the example and the PydanticAI docs.

How can I test custom or third-party MCP servers?

Use the MCP Inspector tool (`npx @modelcontextprotocol/inspector`) and pass the server command (e.g., `uvx mcp-server-fetch`) after the `--` separator. Ensure the required runtime and dependencies are available in your environment.

Does the server require a constant internet connection?

The server communicates with external LLM providers via API calls, so internet access is needed for those requests. MCP servers may run locally or remotely depending on their configuration.

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