Cupcake MCP Server
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Cupcake MCP Serverfind orders for chocolate cupcakes"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Cupcake MCP Server + Wasmer
This example shows how to run a Model Context Protocol (MCP) server for ChatGPT on Wasmer Edge.
ℹ️ MCP servers connected to ChatGPT should expose at least two tools—
searchandfetch—so ChatGPT can both discover content and then retrieve specific items.
Demo
https://mcp-chatgpt-starter.wasmer.app/sse
Add it to ChatGPT as a connector (no auth), and then just ask ChatGPT to interact with it:
How many cupcakes Alice ordered?Related MCP server: Bakery Data MCP Server
How it Works
All logic lives in server.py, but you can think of it in sections:
Data Section
The server loads cupcake records from a local records.json file and builds a lookup dictionary:
RECORDS = json.loads(Path(__file__).with_name("records.json").read_text())
LOOKUP = {r["id"]: r for r in RECORDS}Models Section
We define Pydantic models to structure responses:
SearchResultandSearchResultPagefor search results.FetchResultfor full cupcake order details.
Tools Section
Two MCP tools are exposed via FastMCP:
search(query: str)Splits the query into tokens, performs keyword matching acrosstitle,text, andmetadata, and returns a list of matching results.fetch(id: str)Retrieves a single cupcake order by ID from the lookup dictionary and returns full details, including optionalurlandmetadata.
Entrypoint Section
At the bottom of server.py, the app is created and run:
app = create_server()
if __name__ == "__main__":
app.run(transport="sse")The server uses Server-Sent Events (SSE) to communicate with ChatGPT’s MCP integration.
Running Locally
Install dependencies:
pip install -r requirements.txtRun the server:
python server.pyYour MCP server will now be running and ready for connections from an MCP client (like ChatGPT with MCP enabled).
Example Tools in Action
Search tool (
search("red velvet")) Returns a list of cupcake orders that mention “red velvet.”Fetch tool (
fetch("42")) Returns the full details of order42, including text, metadata, and an optional URL.
Deploying to Wasmer Edge (Overview)
Include both
server.pyandrecords.jsonin your project.Deploy to Wasmer Edge, ensuring the entrypoint is
server.py.Access it at:
https://<your-subdomain>.wasmer.app/sse
This server cannot be deployed
Maintenance
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