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Cupcake MCP Server + Wasmer

This example shows how to run a Model Context Protocol (MCP) server for ChatGPT on Wasmer Edge.

ℹ️ ChatGPT expects MCP servers to expose at least two tools (search and fetch) so it can discover and retrieve content.

Demo

https://mcp-chatgpt-starter.wasmer.app/sse

Add it inside ChatGPT’s “Model Context Protocol” settings (no auth required) and ask questions like “How many cupcakes did Alice order?”

How it Works

Everything lives in main.py:

  • RECORDS = ... loads records.json and builds a LOOKUP dictionary keyed by cupcake order IDs.
  • Pydantic models (SearchResult, SearchResultPage, FetchResult) shape the tool responses so clients get structured JSON.
  • FastMCP(name="Cupcake MCP") registers two tools:
    • search(query) tokenises the query and performs keyword matching across order titles, text, and metadata.
    • fetch(id) returns the full order record or raises ValueError if the ID is unknown.
  • app = create_server() exposes the MCP instance for Wasmer (main:app), and the __main__ block runs it with the SSE transport.

Running Locally

python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
python main.py

Your server listens for SSE connections. Point an MCP client (ChatGPT, Claude Desktop, etc.) at http://127.0.0.1:8000/sse.

Deploying to Wasmer (Overview)

  1. Ship main.py and records.json together; expose the MCP instance as main:app.
  2. Deploy to Wasmer Edge.
  3. Connect your MCP client to https://<your-subdomain>.wasmer.app/sse.