Remote Clinic MCP Server

Stateless Model Context Protocol server developed for the remote-server requirement of the CC3067 Networks project. It publishes a laboratory preparation catalog, evaluates preparation conditions, and returns the server time over Streamable HTTP.

All policies are synthetic and intended only for academic demonstration.

Features

• Streamable HTTP MCP transport.

• Stateless JSON responses suitable for multiple clients.

• Laboratory policies for GLU, HBA1C, CHOL, CREA, and TSH.

• Validation of fasting, alcohol, and strenuous-exercise preparation.

• Health endpoint for local containers and cloud platforms.

• Automated catalog and time-zone tests.

Related MCP server: Veridexa MCP Gateway

Deployed service

The MCP endpoint accepts protocol requests with POST. The health endpoint accepts GET and returns:

{"status": "ok", "server": "remote-clinic-mcp"}

The Cloud Run service is public for the classroom interoperability demonstration. It does not store patient data and does not require an API key.

MCP specification

Tools

test_code is normalized to uppercase. Negative hour values and unknown codes return MCP tool errors. Omitting a preparation value does not satisfy a nonzero requirement.

Demonstration policies

Water is allowed for every policy in the demonstration catalog.

Requirements

• Python 3.11 or newer

• uv

• Docker Desktop for container testing

• Google Cloud CLI and a billing-enabled project for deployment

Local installation

Install the locked environment and start the server:

uv sync --locked
uv run remote-clinic-mcp

The local MCP endpoint is http://localhost:8080/mcp and the health endpoint is http://localhost:8080/health. Set PORT before startup to use another port.

Run the automated tests with:

uv run pytest

Container

Build and run the same image used by the cloud service:

docker build -t remote-clinic-mcp .
docker run --rm -p 8080:8080 remote-clinic-mcp

In another terminal, verify it:

Invoke-RestMethod "http://localhost:8080/health"

Google Cloud Run deployment

Select a Google Cloud project and enable the required services:

gcloud config set project YOUR_PROJECT_ID
gcloud services enable run.googleapis.com cloudbuild.googleapis.com artifactregistry.googleapis.com

Deploy from the repository directory:

gcloud run deploy remote-clinic-mcp `
  --source . `
  --region us-central1 `
  --allow-unauthenticated `
  --port 8080 `
  --min-instances 0 `
  --max-instances 1 `
  --cpu 1 `
  --memory 512Mi `
  --concurrency 20 `
  --timeout 300

The zero minimum allows the service to scale down when idle. Verify the URL printed by Cloud Run by appending /health.

Client configuration

The console host reads the complete MCP URL from its .env file:

REMOTE_MCP_URL=https://remote-clinic-mcp-915028065261.us-central1.run.app/mcp

Any Streamable HTTP MCP client can use the same /mcp endpoint. A representative tool scenario is:

1. Call get_remote_server_time and verify transport is streamable-http.

2. Call get_lab_preparation with {"test_code": "GLU"}.

3. Call check_lab_preparation with {"test_code": "GLU", "fasting_hours": 10, "hours_since_alcohol": 30, "hours_since_strenuous_exercise": 14}.

4. Verify that ready_according_to_demo_policy is true.

Wireshark capture

Cloud Run communicates over TLS on TCP port 443. A capture of a complete MCP session contains:

1. DNS resolution for the Cloud Run hostname.

2. TCP connection establishment.

3. TLS negotiation and encrypted application data.

4. MCP initialization through JSON-RPC.

5. notifications/initialized synchronization.

6. tools/list discovery.

7. tools/call requests and their responses.

The JSON-RPC body remains encrypted unless the client exports TLS session keys and Wireshark is configured to use that key log. Session key files should be kept private and must not be published in the repository.

Source structure

src/remote_clinic_mcp/
  catalog.py   Preparation policies and validation
  server.py    MCP tools, health route, and HTTP entry point
tests/
Dockerfile

Security and cost controls

• The service is stateless and stores no personal or clinical records.

• The catalog contains demonstration data only.

• Cloud Run is limited to one instance and can scale to zero.

• The public endpoint is appropriate only for this academic demonstration.

• After evaluation, disable or delete the service if it is no longer needed.