MCP Forge

🚀 MCP Server Documentation

MCP (Multi-Cloud Processing) Server is a high-performance multi-cloud processing server designed to coordinate and manage computing tasks across multiple cloud platforms. It offers a rich set of API interfaces, supports various protocols and integration solutions, and is suitable for diverse and complex cloud environments.

🚀 Quick Start

Prerequisites

• Operating System: Linux (recommended) or macOS
• Python Version: 3.6+
• Dependency Management Tools: poetry or pipenv (optional)
• Additional Tools: Docker, kubectl (required for containerized deployment)

Installation Steps

1. Clone the project repository:

git clone https://github.com/yourusername/mcp-server.git
cd mcp-server

2. Create a virtual environment and install dependencies:

python -m venv .venv
source .venv/bin/activate  # On macOS/Linux
pip install -r requirements.txt

3. Configure the project:
   • Modify the cloud service provider credentials and API endpoints in config.py.
   • Update the logging configuration to ensure proper output.
4. Start the server:

uvicorn main:app --host 0.0.0.0 --port 8000

Access the Service

• FastAPI Endpoint: http://localhost:5000/predict
• MCP Tool: http://localhost:5000/mcp

✨ Features

Core Features

• Cross-Cloud Platform Compatibility: Supports major cloud service providers such as AWS, GCP, and Azure.
• Load Balancing and Failover: Automatically distributes tasks to different cloud instances to ensure high availability.
• Logging and Monitoring Integration: Built-in support for Prometheus and Grafana for real-time monitoring and analysis.
• Security Authentication: Supports multiple authentication mechanisms such as OAuth2 and JWT to ensure data security.

Advanced Features

• Distributed Transaction Management: Ensures transaction consistency across cloud services.
• Caching and Acceleration: Integrates Redis or Memcached for result caching to improve response speed.
• Dynamic Scaling: Automatically adjusts resource usage based on load to optimize costs.
• Intelligent Routing: Distributes requests to the best available nodes based on geographical location or policies.

📦 Installation

Install Dependencies

pip install mcp-server

Configuration File

Create a config.yaml file as follows:

cloud_providers:
  - name: AWS
    region: us-east-2
    access_key: YOUR_AWS_KEY
    secret_key: YOUR_AWS_SECRET

  - name: GCP
    region: europe-west1
    project_id: your-gcp-project
    key_path: path/to/service-account.json

  - name: Azure
    region: eastus
    subscription_id: your_azure_subscription_id
    client_id: your_azure_client_id
    client_secret: your_azure_client_secret

Start the Server

python3 -m mcp_server.run --config config.yaml

💻 Usage Examples

Basic Usage

# Create a basic MCP server using FastAPI and FastAPI-MCP
from fastapi import FastAPI
from fastapi_mcp import FastApiMCP

app = FastAPI()

# Define a standard FastAPI endpoint
@app.post("/predict", operation_id="predict_sentiment")
async def predict_sentiment(text: str):
    return {"sentiment": "positive", "confidence": 0.92}

# Create and mount the MCP service, automatically converting the above endpoint to an MCP tool
mcp = FastApiMCP(
    app,
    name="sentiment-analysis",
    description="Sentiment analysis service",
    base_url="http://localhost:5000",
    include_operations=["predict_sentiment"]
)

# Mount the MCP service to the specified path
mcp.mount(mount_path="/mcp")

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=8000)

Advanced Usage

# Dependency Injection
from config.database import get_db

@app.get("/items/")
async def get_items(db=Depends(get_db)):
    return db.query(Item).all()

# Environment Adaptation
class DatabaseService:
    def __init__(self, settings):
        self.settings = settings
        self.db = connect_to_db(settings.database_url)

    async def query(self, sql: str) -> list:
        pass

# Logging and Monitoring
import logging
from fastapi import FastAPI
from prometheus_fastapi_instrumentator import Instrumentator

app = FastAPI()

Instrumentator.instrument(app)

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

📚 Documentation

Project Structure

A typical MCP server project may include the following files and directories:

mcp_server/
├── main.py                # Main application file
├── app/                  # FastAPI application module
│   ├── models.py         # Data model definitions
│   ├── services/        # Service classes (e.g., database connection, business logic)
│   └── endpoints/       # API endpoint implementations
├── config/               # Configuration management
│   ├── settings.py      # Application settings
│   └── environment.py   # Environment variable management
└── tests/                # Test cases
    ├── test_endpoints.py  # Unit tests
    └── integration_tests.py  # Integration tests

Common API Endpoints

Sentiment Analysis

# app/endpoints/sentiment_endpoints.py
from fastapi import APIRouter, Depends
from app.services.sentiment_service import SentimentService

router = APIRouter()

@router.post("/sentiment")
async def analyze_sentiment(text: str, service: SentimentService = Depends(SentimentService)):
    sentiment = await service.analyze(text)
    return {"sentiment": sentiment}

Parking Search Service

# app/endpoints/parking_endpoints.py
from fastapi import APIRouter, Depends
from app.services.parking_service import ParkingService

router = APIRouter()

@router.get("/parking/nearby")
async def find_nearby_parkings(latitude: float, longitude: float, radius: int,
                              service: ParkingService = Depends(ParkingService)):
    result = await service.find_nearby_parkings(latitude, longitude, radius)
    return {"data": result}

Personalized Recommendation System

# app/endpoints/recommendation_endpoints.py
from fastapi import APIRouter, Depends
from app.services.recommendation_service import RecommendationService

router = APIRouter()

@router.post("/recommendations")
async def get_recommendations(user_id: int, service: RecommendationService = Depends(RecommendationService)):
    recommendations = await service.get_recommendations(user_id)
    return {"recommendations": recommendations}

Advanced Features

Distributed Transaction Management

from mcp_server.transaction import TransactionManager

tm = TransactionManager()
with tm.context():
    # Perform cross-cloud operations
    pass

Intelligent Routing and Load Balancing

class SmartRouter:
    def route_request(self, request):
        # Return the target cloud service provider and region
        pass

Dynamic Scaling

# Kubernetes Horizontal Pod Autoscaler configuration
apiVersion: autoscaling/v1
kind: HorizontalPodAutoscaler
metadata:
  name: mcp-server-hpa
spec:
  scaleRef:
    kind: Deployment
    name: mcp-server-deployment
    apiVersion: apps/v1
  minReplicas: 2
  maxReplicas: 5
  targetCPUUtilizationPercentage: 80

🔧 Technical Details

Service Class Creation

# app/services/parking_service.py
class ParkingService:
    def __init__(self, db):
        self.db = db

    async def find_nearby_parkings(self, latitude: float, longitude: float, radius: int) -> list:
        # Query the database to get nearby parking lots
        pass

API Endpoint Implementation

# app/endpoints/parking_endpoints.py
from fastapi import APIRouter, Depends
from app.services.parking_service import ParkingService
from config.environment import get_db

router = APIRouter()

@router.get("/parking/nearby")
async def find_nearby_parkings(latitude: float, longitude: float, radius: int,
                              service: ParkingService = Depends(ParkingService)):
    db = next(get_db())
    result = await service.find_nearby_parkings(latitude, longitude, radius)
    return {"data": result}

Configuration Management

# config/settings.py
class Settings:
    def __init__(self):
        self.debug = True  # Debug mode, default is True

    @property
    def database_url(self) -> str:
        return "sqlite:///./test.db"

    @property
    def secret_key(self) -> str:
        return "your-secret-key-here"

📄 License

No license information provided in the original document.

Troubleshooting

Common Issues

• Service fails to start: Check the configuration file for correctness, ensure all dependencies are installed, and review the log output.
• Cross-cloud communication is interrupted: Verify the API keys and credentials of each cloud platform, and check the network connection.
• Poor performance: Optimize the query logic, add a caching mechanism, or consider horizontal scaling.

Logging and Debugging

MCP Server supports structured logging, which facilitates problem troubleshooting. You can use the logging module or integrate third-party tools such as sentry for real-time monitoring.

Performance Optimization

Bottleneck Analysis

• Database Queries: Reduce complex queries and add caching.
• Network Latency: Optimize the data transfer protocol, use CDN or edge computing.
• Resource Allocation: Configure cloud resources reasonably to avoid over - or under - utilization.

Tool Recommendations

• Profiling Tools: Use cProfile to analyze the performance of Python programs.
• Load Testing: Use locust or k6 for stress testing to ensure service stability.

Deployment and Scaling

Containerized Deployment

1. Create a Dockerfile:

FROM python:3.9-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
EXPOSE 8000
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

2. Build the image:

docker build -t mcp-server .

3. Run the container:

docker run -p 8000:8000 --name mcp-server-container mcp-server

4. Kubernetes Deployment: Create Deployment and Service files and deploy them to the Kubernetes cluster.

Elastic Scaling

By setting up automatic scaling policies (such as Horizontal Pod Autoscaler), MCP Server can automatically adjust resource usage based on the actual load, reducing costs and improving efficiency.

Best Practices

1. Modular Design: Split functions into independent modules or services for easy maintenance and expansion.
2. Dependency Management: Use tools like poetry or pipenv to manage Python dependencies and ensure environment consistency.
3. Configuration Management: Use configuration files instead of hard - coded values for easy deployment in different environments.
4. Logging: Integrate structured logging libraries such as logging or sentry for easy debugging and problem analysis.
5. Test - Driven Development: Write unit tests, integration tests, and end - to - end tests to ensure code quality.

Project Summary

By following the above steps, you can build an efficient and reliable MCP Server. From modular API design to performance optimization and expansion, every detail is crucial. Using Docker and Kubernetes for containerized deployment, and combining Prometheus and Grafana for monitoring and visualization will make your service more stable and easier to manage.