Petclinic

The Petclinic MCP Server provides programmatic access to the Swagger Petstore PetClinic APIs, exposing specific capabilities you can invoke from AI models. It enables you to fetch pets by status and integrate pet data into your workflows or chatbot interactions.

How to use

You can use the Petclinic MCP Server by connecting your MCP client to either a remote HTTP endpoint or to local MCP processes that run on your machine. The server exposes fetch_petsByStatus to retrieve pets with status values such as available, pending, or sold. Use your MCP client to call this capability and then incorporate the results into your AI-driven responses, dashboards, or automation flows.

How to install

Prerequisites you need before starting are the uv package manager and Python. Follow these steps to install and run the MCP server locally, or set up the included MCP configurations.

Clone the project and navigate to its directory, then initialize the MCP environment using uv.

Create a virtual environment and activate it.

Install dependencies for MCP support.

Launch the MCP inspector if you want to visualize or inspect the interactions during development.

Alternatively, run the MCP server directly without the inspector.

Additional configuration and notes

You can deploy and configure the Petclinic MCP Server through both HTTP endpoints and local stdio processes. The following examples show how to expose the server via a remote URL and how to run local MCP components that back the server.

Available tools

fetch_petsByStatus

Fetches pets from the Petstore by their status, returning either available, pending, or sold pets as results for downstream AI processing.