# 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**—`search` and `fetch`—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?
```
## 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:
```python
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:
* `SearchResult` and `SearchResultPage` for search results.
* `FetchResult` for full cupcake order details.
### Tools Section
Two MCP tools are exposed via `FastMCP`:
* **`search(query: str)`**
Splits the query into tokens, performs keyword matching across `title`, `text`, and `metadata`, 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 optional `url` and `metadata`.
### Entrypoint Section
At the bottom of `server.py`, the app is created and run:
```python
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:
```bash
pip install -r requirements.txt
```
Run the server:
```bash
python server.py
```
Your 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 order `42`, including text, metadata, and an optional URL.
## Deploying to Wasmer Edge (Overview)
1. Include both `server.py` and `records.json` in your project.
2. Deploy to Wasmer Edge, ensuring the entrypoint is `server.py`.
3. Access it at:
`https://<your-subdomain>.wasmer.app/sse`
