ailab_rip_mcp_app

A foundational demo of a Model Context Protocol (MCP) app. A tiny FastMCP server exposes a stock-quote tool from a bundled JSON file, runs locally over streamable HTTP, deploys to AWS via ECS Express Mode, and plugs into ChatGPT as a Developer Mode custom connector.

The point is the walkthrough, not the depth. The code is short enough to read in one sitting.

What is MCP

The Model Context Protocol is an open standard for wiring LLMs to external tools and data. An MCP server exposes capabilities — most commonly tools (functions the model can call) — and an MCP client (ChatGPT, Claude, your own agent) discovers and invokes them over a transport such as stdio or streamable HTTP. This repo is an MCP server.

Related MCP server: AgentCore MCP Server

Prerequisites

• Python 3.10+

• uv for dependency management (brew install uv or curl -LsSf https://astral.sh/uv/install.sh | sh)

• Docker (for the AWS deploy)

• AWS CLI v2 with a configured profile that can create ECR repos and ECS services

• Node.js (only for npx @modelcontextprotocol/inspector during local verification)

• A ChatGPT account that can enable Developer Mode — see the ChatGPT integration section

Repo tour

.
├── src/
│   ├── server.py          # FastMCP server, two tools + widget resource
│   └── data/quotes.json   # 10 hand-curated tickers
├── web/                   # React + Vite widget (Apps SDK compatible)
│   ├── src/               #   StockCard.tsx, main.tsx, styles.css
│   ├── dist/stock-card.js #   built bundle (committed — server needs no npm)
│   └── package.json
├── tests/                 # pytest + in-memory FastMCP Client
├── scripts/push_to_ecr.sh # build + push helper
├── Dockerfile             # uv-based image, exposes 8000
├── pyproject.toml         # uv-managed deps
└── uv.lock                # locked deps (committed)

Install and test

uv sync                # create .venv from uv.lock
uv run pytest          # 4 tests, in-memory, ~40 ms

Run locally and verify with MCP Inspector

uv run python src/server.py
# → FastMCP banner shows http://0.0.0.0:8000/mcp

In another shell:

npx @modelcontextprotocol/inspector

In the Inspector UI: Transport = Streamable HTTP, URL = http://localhost:8000/mcp, then invoke:

• list_supported_symbols → 10 tickers

• get_stock_quote with symbol="AAPL" → the seeded dict + day_change_pct

• get_stock_quote with symbol="ZZZZ" → a tool error naming the supported symbols

Deploy to AWS with ECS Express Mode

ECS Express Mode is AWS's newest single-command path for containerized services. It auto-provisions the ECS cluster, Fargate task, ALB with HTTPS, target group, security groups, and auto-scaling from one API call — exactly what a ChatGPT connector needs.

1. Build and push the image to ECR

./scripts/push_to_ecr.sh
# → prints IMAGE_URI=<account>.dkr.ecr.<region>.amazonaws.com/mcp-stock-quote:latest

Overridable env vars: AWS_REGION, AWS_PROFILE, ECR_REPO_NAME, IMAGE_TAG. The script builds for linux/amd64 explicitly — required for Fargate x86 tasks when the dev machine is Apple silicon.

2. Deploy to ECS Express (via glab_iac)

Infrastructure lives in the sibling glab_iac Terraform repo. Add a new ECS Express block that points to the ECR image you just pushed, then apply:

1. In glab_iac, add a new ECS Express service block. Set:

   • image → the IMAGE_URI printed by push_to_ecr.sh.

   • container_port → 8000 (what the FastMCP server binds to).

   • health_check_path → /health.

2. terraform plan and review — Express Mode auto-provisions the cluster, Fargate task, ALB with HTTPS, target group, security groups, IAM roles, and auto-scaling.

3. terraform apply. Wait until the service reports RUNNING and its ALB target group reports healthy (a few minutes).

4. Grab the public HTTPS URL from the Terraform output (or the ECS console). Your MCP endpoint is https://<generated-domain>/mcp.

Verify with MCP Inspector or a quick curl before wiring ChatGPT:

curl -s https://<generated-domain>/health   # → {"status":"ok"}

Then integrate it as a custom connector — see the next section.

Region caveat: ECS Express Mode is new (announced late 2025) and not yet available in every AWS region. Check the ECS console in your region.

Integrate with ChatGPT (Developer Mode)

All quotes below come from OpenAI's custom MCP docs.

Requirements

• ChatGPT subscription — Developer Mode is a gated feature. OpenAI's developer docs don't spell out the tier list; the authoritative source is help.openai.com article 11487775. Historically: Plus, Pro, Business, Enterprise, Edu. Free tier cannot follow this section.

• Developer Mode toggled on —

  "In ChatGPT, open Settings → Security and login and turn on Developer mode."

• Server transport — public HTTPS URL, streamable-HTTP transport, path /mcp. ECS Express Mode + FastMCP's defaults give you all three.

For this demo we use no authentication. OpenAI's docs neither mandate nor explicitly forbid it — a personal Developer Mode server only you connect to works fine unauthenticated, and it keeps the demo focused on MCP itself. This is dev-only. Production connectors should use OAuth.

Add the connector

1. ChatGPT → Menu → Plugins → Add (+).

2. Paste https://<ecs-express-domain>/mcp.

3. Leave authentication empty: No Auth (dev-only).

4. Save.

5. Connect.

6. Check at Plugins (browse until you find yours).

Try it

Prompt ChatGPT:

Using the stock quote connector, what is AAPL's current price? @stock_mcp_app what is AAPL's current price?

The trace panel should show a call to list_supported_symbols and/or get_stock_quote. Both tools are read-only, so no confirmation prompt appears.

The widget (sharing UI back to ChatGPT)

Chapter one served pure JSON and let ChatGPT paint a paragraph. Chapter two attaches a small React "stock card" to every get_stock_quote result so ChatGPT renders it inline. Two pieces make this work:

1. The tool declares its template and a typed return. In src/server.py:

   @mcp.tool(meta={"openai/outputTemplate": "ui://stock-card/v1.html"})
   def get_stock_quote(symbol: str) -> Quote:
       ...

   Two things matter here: the _meta field points at the widget resource, and the Quote TypedDict return makes FastMCP emit an outputSchema. Without that schema, ChatGPT filters window.openai.toolOutput down to a handful of fields — the widget wouldn't see the price.

2. The resource ships the widget. Same file:

   @mcp.resource("ui://stock-card/v1.html", mime_type="text/html;profile=mcp-app")
   def stock_card_widget() -> str:
       return _WIDGET_HTML  # <div id="root"></div> + inlined React bundle

   The mime type text/html;profile=mcp-app is what tells ChatGPT to render this inside a sandboxed iframe next to the assistant reply. The React bundle in web/dist/stock-card.js reads window.openai.toolOutput (found by walking up the frame chain to the host frame) and paints a price card.

Editing the widget. Only needed if you want to change the UI itself — the server ships the built bundle:

cd web
npm install
npm run build   # -> web/dist/stock-card.js

The bundle is committed on purpose so uv run python src/server.py works without a JS toolchain. Rebuild the container after any widget change so the new bundle ships to ECS. In ChatGPT, hit Refresh on the connector (or remove + re-add) so it picks up any schema changes, then start a new chat.

Gotchas we hit. Two Apps SDK details worth knowing:

• Vite's lib mode doesn't replace process.env.NODE_ENV. Without define: { "process.env.NODE_ENV": '"production"' } in vite.config.ts, the widget throws ReferenceError: process is not defined in the sandbox.

• window.openai is only injected on the outer host frame. Widgets rendered in a nested iframe must walk up (window.parent, window.top) to reach it — see web/src/main.tsx.

Limitations and next steps

• Static data — real prices need something like yfinance; note its ToS.

• No auth — add OAuth via FastMCP's built-in auth providers before making the server public.

• No infrastructure-as-code — Express Mode is one CLI call. For repeatable environments, port to CloudFormation (aws-samples/sample-mcp-server-on-ecs) or CDK.

• Single-stage Docker image — split builder/runtime stages to shrink the image.

Links

• FastMCP docs — https://gofastmcp.com

• OpenAI custom MCP connectors — https://developers.openai.com/api/docs/mcp

• ECS Express Mode launch post — https://aws.amazon.com/blogs/aws/build-production-ready-applications-without-infrastructure-complexity-using-amazon-ecs-express-mode/

• Deploying MCP on ECS (AWS blog) — https://aws.amazon.com/blogs/containers/deploying-model-context-protocol-mcp-servers-on-amazon-ecs/

• uv — https://astral.sh/uv