Container MCP Server

A Model Context Protocol (MCP) server designed for containerized deployment with HTTP transport. This server provides simple, dependency-free tools and prompts that can be used by MCP clients via streamable HTTP transport.

Features

• HTTP Transport: Uses streamable HTTP transport for remote MCP server deployment

• Container Ready: Optimized for Docker/Kubernetes deployment with health checks

• Simple Tools: Weather data, mathematical calculations, and context-aware operations

• Prompts: Reusable templates for weather reports and calculations

• No External Dependencies: Mock data for easy testing and demonstration

Related MCP server: MCP Server

Tools

1. get_weather

Get mock weather information for a city.

Parameters:

• city (string, optional): City name (default: "San Francisco")

Returns: Weather data including temperature, condition, and humidity

2. sum_numbers

Add two numbers together.

Parameters:

• a (float): First number

• b (float): Second number

Returns: The sum of the two numbers

3. context_info

Demonstrate MCP context capabilities including logging, progress reporting, and metadata access.

Parameters:

• message (string): A message to process

• ctx (Context): MCP Context object (automatically injected)

Returns: Information about the context and processing

Prompts

1. weather_report

Generate weather report prompts for specified cities.

Arguments:

• city (string): City name for the weather report

• format (string): Report format ("brief", "detailed", or "forecast")

2. calculation_helper

Generate prompts for mathematical calculations.

Arguments:

• operation (string): Type of mathematical operation

• context (string): Additional context for the calculation

Installation & Development

Using Virtual Environment (Recommended)

1. Create and activate virtual environment:

   # Create virtual environment
   python -m venv venv
   
   # Activate virtual environment
   # On Unix/macOS:
   source venv/bin/activate
   # On Windows:
   # venv\Scripts\activate

2. Install dependencies:

   pip install -r requirements.txt

3. Run in development mode:

   python -m src.server --port 8000 --log-level DEBUG

4. Run tests:

   pytest

5. Deactivate virtual environment when done:

   deactivate

Without Virtual Environment (Not Recommended)

1. Install dependencies:

   pip install -r requirements.txt

2. Run in development mode:

   python -m src.server --port 8000 --log-level DEBUG

3. Run tests:

   pytest

Direct Execution

The server supports direct execution for development and testing:

# Basic execution
python src/server.py

# With custom options
python src/server.py --port 3000 --log-level DEBUG --json-response

Command-line options:

• --port: Port to run the server on (default: 8000)

• --log-level: Logging level (default: INFO)

• --json-response: Use JSON responses instead of SSE streams

Container Deployment

Docker

1. Build the container:

   docker build -t mcp-server .

2. Run the container:

   docker run -p 8000:8000 mcp-server

3. With custom environment:

   docker run -p 8000:8000 -e LOG_LEVEL=DEBUG mcp-server

Docker Compose

1. Basic deployment:

   docker-compose up

2. With production nginx proxy:

   docker-compose --profile production up

Kubernetes

Example deployment:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: mcp-server
spec:
  replicas: 3
  selector:
    matchLabels:
      app: mcp-server
  template:
    metadata:
      labels:
        app: mcp-server
    spec:
      containers:
      - name: mcp-server
        image: mcp-server:latest
        ports:
        - containerPort: 8000
        livenessProbe:
          httpGet:
            path: /health
            port: 8000
          initialDelaySeconds: 30
          periodSeconds: 10
---
apiVersion: v1
kind: Service
metadata:
  name: mcp-service
spec:
  selector:
    app: mcp-server
  ports:
  - port: 80
    targetPort: 8000
  type: LoadBalancer

API Endpoints

Health Check

• URL: GET /health

• Response: Server health status and metadata

• Use: Container orchestration health checks

Server Info

• URL: GET /

• Response: Server information, available tools, and prompts

• Use: Discovery and documentation

MCP Endpoint

• URL: POST /mcp

• Protocol: MCP over HTTP (JSON-RPC 2.0)

• Transport: Streamable HTTP with SSE support

• Use: MCP client connections

Connection Details

For MCP Clients

Server URL: http://localhost:8000/mcp

Transport: Streamable HTTP

Authentication: None (can be extended)

Example Client Connection (Python)

import asyncio
from mcp import ClientSession
from mcp.client.streamable_http import streamablehttp_client

async def main():
    async with streamablehttp_client("http://localhost:8000/mcp") as (read, write, _):
        async with ClientSession(read, write) as session:
            await session.initialize()
            
            # List available tools
            tools = await session.list_tools()
            print(f"Available tools: {[tool.name for tool in tools.tools]}")
            
            # Call a tool
            result = await session.call_tool("get_weather", {"city": "New York"})
            print(f"Weather result: {result}")

if __name__ == "__main__":
    asyncio.run(main())

Testing

Unit Tests

pytest tests/

Integration Tests

# Start the server
python -m src.server --port 8001 &
SERVER_PID=$!

# Test health endpoint
curl http://localhost:8001/health

# Test server info
curl http://localhost:8001/

# Test MCP connection with a client
# (see example above)

# Cleanup
kill $SERVER_PID

Container Tests

# Test container build
docker build -t mcp-server-test .

# Test container run
docker run -d -p 8002:8000 --name mcp-test mcp-server-test

# Test health check
curl http://localhost:8002/health

# Cleanup
docker stop mcp-test && docker rm mcp-test

Monitoring

Health Checks

The server provides a /health endpoint that returns:

• Server status

• Tool and prompt counts

• Transport information

Logging

Structured logging with configurable levels:

# Set log level via environment
export LOG_LEVEL=DEBUG
python -m src.server

# Or via command line
python -m src.server --log-level DEBUG

Metrics

For production deployments, consider adding:

• Prometheus metrics endpoint

• OpenTelemetry tracing

• Request/response logging

Architecture

┌─────────────────┐    HTTP/SSE     ┌─────────────────┐
│   MCP Client    │ ◄──────────────► │   MCP Server    │
│                 │                 │                 │
│ - Claude Code   │                 │ - Tools         │
│ - Custom Client │                 │ - Prompts       │
│ - Web App       │                 │ - Health Check  │
└─────────────────┘                 └─────────────────┘
                                           │
                                           ▼
                                    ┌─────────────────┐
                                    │   Container     │
                                    │                 │
                                    │ - Docker        │
                                    │ - Kubernetes    │
                                    │ - Cloud Run     │
                                    └─────────────────┘

Security Considerations

• The server runs as a non-root user in containers

• No secrets or API keys are required for basic functionality

• Consider adding authentication for production deployments

• Network policies should restrict access to necessary ports only

Contributing

1. Fork the repository

2. Create a feature branch

3. Make changes with tests

4. Run the test suite

5. Submit a pull request

License

This project is available under the MIT License.