Petclinic

Petclinic MCP server uses petclinic v2 apis (https://petstore.swagger.io/). It interacts with the Swagger Petstore API (similar to a "PetClinic") and exposes tools to be used by OpenAI models.

npx @modelcontextprotocol/inspector uv run petclinic_mcp_server.py

You will need to supply a configuration for the server for your MCP Client. Here's what the configuration looks like forclaude_desktop_config.json:

{ "mcpServers": { "filesystem": { "command": "npx", "args": [ "-y", "@modelcontextprotocol/server-filesystem", "/{your-project-path}/petclinic-mcp/" ] }, "research": { "command": "/{your-uv-install-path}/uv", "args": [ "--directory", "/{your-project-path}/petclinic-mcp/", "run", "petclinic_mcp_server.py"] }, "fetch": { "command": "uvx", "args": ["mcp-server-fetch"] } } }

- tip use sse transport to deploy petclinic mcp server as a remote server. Change petclinic_mcp_server.py line 39 to use stdio transport

- Login to your Cloud Foundry account and push the application

Model Context Protocol (MCP) servers are lightweight programs that expose specific capabilities to AI models through a standardized interface. These servers act as bridges between LLMs and external tools, data sources, or services, allowing your AI application to perform actions like searching databases, accessing files, or calling external APIs without complex custom integrations.

Create a user-provided service that provides the URL for an existing MCP server:

cf cups petclinic-mcp-server -p '{"mcpServiceURL":"https://your-petclinic-mcp-server.example.com"}'

Bind the MCP service to your application:

cf bind-service ai-tool-chat petclinic-mcp-server

Your chatbot will now register with the research MCP agent, and the LLM will be able to invoke the agent's capabilities when responding to chat requests.

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