What is required to run the examples?
You need Python and `uv`. The project also assumes you have a local Ollama instance available for the LLM models.
PydanticAI MCP Experiment
@eschmidt42
Basic information
About PydanticAI MCP Experiment
Minimalist examples to provide your own MCP servers to your local llm models.
PydanticAI MCP Experiment config
No standard config provided
This server doesn't expose a parseable MCP config block in its README. See the repository for install instructions.
RepositoryPydanticAI MCP Experiment tools
Tool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
Overview
What is PydanticAI MCP Experiment?
PydanticAI MCP Experiment provides minimalist examples for running your own MCP servers with local LLM models. It uses pydantic_ai and local models via Ollama, offering four different transport configurations under the examples/ folder. This project is aimed at developers who want to get hands‑on experience with MCP clients and servers.
How to use PydanticAI MCP Experiment?
The project is managed with uv. After cloning, run uv sync to install dependencies. Then change into any example folder (e.g., examples/0_subprocess) and start the server with python server.py, followed by python client.py in another terminal. The setup uses Logfire for logging, which will prompt for authentication on first run.
Key features of PydanticAI MCP Experiment
- Four transport examples: stdio, HTTP via mcp.run, FastAPI, and Starlette
- Uses
pydantic_aiand local Ollama models - Minimal, self‑contained client/server pairs per example
- Logging integration with Logfire (optional)
- Managed with
uvfor reproducible environments
Use cases of PydanticAI MCP Experiment
- Experimenting with MCP servers on a local machine
- Comparing different transport protocols (stdio vs HTTP)
- Prototyping MCP‑based AI assistants with
pydantic_ai - Learning how to integrate MCP with local LLM models
Frequently asked questions
How do I set up the project?
Run `uv sync` in the root directory to install all dependencies. No additional configuration is required.
How do I run an example?
Change to the example folder (e.g., `examples/0_subprocess`), start the server with `python server.py`, then in another terminal run `python client.py`.
What logging is used and can I change it?
The project uses Logfire by default. You will be asked to authenticate on first run. You can switch to an alternative OpenTelemetry backend like `otel‑tui`, but the README notes that prompt and response messages may not be displayed that way.
What MCP transport protocols are available?
The examples include stdio (via subprocess) and three HTTP variants: using `mcp.run`, FastAPI, and Starlette.
More Other MCP servers
Glasswarp
GlasswarpSee and control a real Windows PC you own — from any MCP client, locally or remotely. Observe (UIA + screenshots), click/type/drag/scroll, launch apps, owner Live View. BYOH: your machine, your key.
Health by Dr Brad
Dr Brad StanfieldHealth by Dr Brad works out a health plan from your own results: which screening is due at your age, and evidence-based suggestions, each with the reason and the published guideline or study behind it. Your record is one
MaxKB
1Panel-dev🔥 MaxKB is an open-source platform for building enterprise-grade agents. 强大易用的开源企业级智能体平台。
22.0K
YOUB
Connect your AI assistant to your own YOUB endurance training account. Read recorded activities with full analysis, planned sessions, race goals, wellness (HRV, sleep, resting HR) and your athletic profile. Create or mov
Codelf
unbugA search tool helps dev to solve the naming things problem.
14.2K
Comments