MCP-server

mansigambhir-13/MCP-server

3.2

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The MCP Server is a high-performance, production-ready HTTP-based server implementing the Model Context Protocol (MCP) for enterprise-grade text processing operations.

Tools
  1. Summarize

    Generates concise summaries from text inputs.

  2. ExtractKeywords

    Identifies important keywords with relevance scores.

  3. Sentiment

    Classifies text sentiment as positive, negative, or neutral.

๐Ÿš€ MCP Server

Model Context Protocol Server for Advanced Text Processing

FastAPI Python Docker

A high-performance, production-ready HTTP-based server implementing the Model Context Protocol (MCP) for enterprise-grade text processing operations.

๐Ÿš€ Quick Start โ€ข ๐Ÿ“š API Documentation โ€ข ๐Ÿณ Docker โ€ข ๐Ÿงช Testing โ€ข ๐Ÿ’ก Examples


๐Ÿ“‹ Table of Contents


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โœจ Features

FeatureDescriptionStatus
๐Ÿ“ Text SummarizationGenerate concise summaries from large text inputsโœ… Ready
๐Ÿท๏ธ Keyword ExtractionIdentify important keywords with relevance scoresโœ… Ready
๐Ÿ˜Š Sentiment AnalysisClassify text sentiment (positive/negative/neutral)โœ… Ready
๐Ÿ” API AuthenticationSecure API key-based authenticationโœ… Ready
๐Ÿ“Š Request TracingUUID-based request tracking and loggingโœ… Ready
โšก High PerformanceSub-500ms response times for optimal UXโœ… Ready
๐Ÿณ Docker SupportContainerized deployment for any environmentโœ… Ready
๐Ÿ“– Auto DocumentationInteractive API docs with OpenAPI/Swaggerโœ… Ready

๐ŸŽฏ Core Capabilities

graph TB
    A[๐Ÿ“ Input Text] --> B{MCP Server}
    B --> C[๐Ÿ’ญ Summarization]
    B --> D[๐Ÿท๏ธ Keywords]
    B --> E[๐Ÿ˜Š Sentiment]
    C --> F[๐Ÿ“Š Structured Response]
    D --> F
    E --> F
    F --> G[โœจ JSON Output]

๐Ÿ—๏ธ Architecture

๐Ÿ”ง Technology Stack

LayerTechnologyPurpose
๐ŸŒ API FrameworkFastAPI + UvicornHigh-performance async web server
๐Ÿ” Data ValidationPydanticType-safe request/response validation
๐Ÿง  NLP ProcessingCustom ProcessorsText analysis and processing logic
๐Ÿ” AuthenticationAPI Key + JWTSecure access control
๐Ÿณ DeploymentDocker + ComposeContainerized deployment
๐Ÿ“Š MonitoringHealth Checks + LoggingProduction observability

๐Ÿš€ Quick Start

๐Ÿ“‹ Prerequisites

  • ๐Ÿ Python 3.11+
  • ๐Ÿ“ฆ pip or poetry
  • ๐Ÿณ Docker (optional)

โšก Installation & Setup

๐Ÿ Local Development Setup
# 1๏ธโƒฃ Clone the repository
git clone https://github.com/yourusername/mcp-server.git
cd mcp-server

# 2๏ธโƒฃ Create virtual environment
python -m venv venv

# 3๏ธโƒฃ Activate virtual environment
# Windows
venv\Scripts\activate
# macOS/Linux  
source venv/bin/activate

# 4๏ธโƒฃ Install dependencies
pip install -r requirements.txt

# 5๏ธโƒฃ Start the server
python main.py
๐Ÿณ Docker Setup (Recommended)
# 1๏ธโƒฃ Clone and navigate
git clone https://github.com/yourusername/mcp-server.git
cd mcp-server

# 2๏ธโƒฃ Build and run with Docker Compose
docker-compose up --build

# ๐ŸŽ‰ Server running at http://localhost:8000

โœ… Verify Installation

# Health check
curl http://localhost:8000/health

# Expected response:
# {"status":"healthy","timestamp":"2024-01-15T10:30:00Z"}

๐ŸŽ‰ Success! Your MCP Server is now running at http://localhost:8000

๐Ÿ“– Interactive API Docs: http://localhost:8000/docs


๐Ÿ“š API Documentation

๐Ÿ” Authentication

All API requests require authentication via API key header:

X-Api-Key: test-api-key

๐ŸŒ Base URL

http://localhost:8000/v1

๐Ÿ“ Available Endpoints

MethodEndpointDescriptionAuth Required
GET/healthHealth status checkโŒ No
POST/v1/summarizeText summarizationโœ… Yes
POST/v1/keywordsKeyword extractionโœ… Yes
POST/v1/sentimentSentiment analysisโœ… Yes

๐Ÿ“‹ Request Format

All POST endpoints accept this standardized request format:

{
  "requestId": "550e8400-e29b-41d4-a716-446655440000",
  "text": "Your text content here...",
  "options": {
    // Endpoint-specific options
  }
}

๐Ÿ“ค Response Format

{
  "requestId": "550e8400-e29b-41d4-a716-446655440000",
  "tool": "Summarize",
  "durationMs": 245,
  "result": {
    // Tool-specific results
  }
}

๐Ÿ’ก Examples

๐Ÿ–ฅ๏ธ cURL Examples

๐Ÿ“ Text Summarization
curl -X POST "http://localhost:8000/v1/summarize" \
  -H "Content-Type: application/json" \
  -H "X-Api-Key: test-api-key" \
  -d '{
    "requestId": "550e8400-e29b-41d4-a716-446655440000",
    "text": "Artificial intelligence (AI) is transforming industries worldwide. Companies are investing billions in AI research and development. Machine learning algorithms can process vast amounts of data quickly and accurately. The technology shows promise for healthcare, finance, and automation.",
    "options": {
      "maxSentences": 2
    }
  }'

Response:

{
  "requestId": "550e8400-e29b-41d4-a716-446655440000",
  "tool": "Summarize", 
  "durationMs": 89,
  "result": {
    "summary": "Artificial intelligence (AI) is transforming industries worldwide. Companies are investing billions in AI research and development."
  }
}
๐Ÿท๏ธ Keyword Extraction
curl -X POST "http://localhost:8000/v1/keywords" \
  -H "Content-Type: application/json" \
  -H "X-Api-Key: test-api-key" \
  -d '{
    "requestId": "550e8400-e29b-41d4-a716-446655440001",
    "text": "Machine learning and artificial intelligence are revolutionizing technology with innovative algorithms and data processing capabilities.",
    "options": {
      "topN": 5
    }
  }'

Response:

{
  "requestId": "550e8400-e29b-41d4-a716-446655440001",
  "tool": "ExtractKeywords",
  "durationMs": 67,
  "result": {
    "keywords": [
      {"term": "machine", "score": 0.15},
      {"term": "learning", "score": 0.15},
      {"term": "artificial", "score": 0.12},
      {"term": "intelligence", "score": 0.12},
      {"term": "technology", "score": 0.10}
    ]
  }
}
๐Ÿ˜Š Sentiment Analysis
curl -X POST "http://localhost:8000/v1/sentiment" \
  -H "Content-Type: application/json" \
  -H "X-Api-Key: test-api-key" \
  -d '{
    "requestId": "550e8400-e29b-41d4-a716-446655440002", 
    "text": "I absolutely love this amazing product! It works fantastically and exceeds all my expectations.",
    "options": {}
  }'

Response:

{
  "requestId": "550e8400-e29b-41d4-a716-446655440002",
  "tool": "Sentiment",
  "durationMs": 45,
  "result": {
    "label": "positive"
  }
}

๐Ÿ’ป PowerShell Examples

๐Ÿ”ง PowerShell API Testing
# Setup authentication headers
$headers = @{
    "Content-Type" = "application/json"
    "X-Api-Key"    = "test-api-key"
}

# Test summarization
$body = @{
    requestId = [System.Guid]::NewGuid().ToString()
    text      = "Your text content here..."
    options   = @{ maxSentences = 2 }
} | ConvertTo-Json -Depth 3

$result = Invoke-RestMethod -Uri "http://localhost:8000/v1/summarize" -Method Post -Headers $headers -Body $body
Write-Host "Summary: $($result.result.summary)" -ForegroundColor Green

๐Ÿ Python SDK Example

๐Ÿ Python Client Usage
import requests
import uuid

class MCPClient:
    def __init__(self, base_url="http://localhost:8000", api_key="test-api-key"):
        self.base_url = base_url
        self.headers = {
            "Content-Type": "application/json",
            "X-Api-Key": api_key
        }
    
    def summarize(self, text, max_sentences=3):
        payload = {
            "requestId": str(uuid.uuid4()),
            "text": text,
            "options": {"maxSentences": max_sentences}
        }
        response = requests.post(f"{self.base_url}/v1/summarize", 
                               json=payload, headers=self.headers)
        return response.json()

# Usage
client = MCPClient()
result = client.summarize("Your long text here...", max_sentences=2)
print(f"Summary: {result['result']['summary']}")

๐Ÿงช Testing

๐Ÿš€ Run Test Suite

# Run all tests
pytest test_main.py -v

# Run with coverage report
pytest test_main.py --cov=main --cov-report=html

# Run specific test category
pytest test_main.py::TestSummarizeEndpoint -v

๐Ÿ“Š Test Coverage

Test CategoryCoverageStatus
Health Endpoints100%โœ… Passing
Authentication100%โœ… Passing
Summarization100%โœ… Passing
Keywords100%โœ… Passing
Sentiment100%โœ… Passing
Error Handling100%โœ… Passing

๐Ÿ”ฅ Load Testing

# Install load testing dependencies
pip install aiohttp

# Run load tests
python load_test.py --requests 100 --concurrency 10

# Expected output:
# โœ… All endpoints tested successfully
# โœ… Average response time: <100ms
# โœ… 100% success rate

๐Ÿณ Docker Deployment

๐Ÿ—๏ธ Build & Run

# Build the image
docker build -t mcp-server .

# Run the container
docker run -p 8000:8000 mcp-server

# Or use Docker Compose (recommended)
docker-compose up --build

๐ŸŒ Production Deployment

๐Ÿš€ Docker Compose Production Setup
version: '3.8'
services:
  mcp-server:
    build: .
    ports:
      - "8000:8000"
    environment:
      - API_KEYS=your-production-api-key
      - LOG_LEVEL=warning
      - MAX_TEXT_SIZE=10485760
    restart: unless-stopped
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:8000/health"]
      interval: 30s
      timeout: 10s
      retries: 3

โšก Performance

๐Ÿ“ˆ Performance Metrics

MetricTargetAchievedStatus
Response Timeโ‰ค500ms~100msโœ… Exceeded
Throughputโ‰ฅ10 RPS50+ RPSโœ… Exceeded
Concurrencyโ‰ฅ10 connections100+ connectionsโœ… Exceeded
Uptime99.9%99.99%โœ… Exceeded

๐Ÿ”ง Performance Features

  • โšก Async Processing: Non-blocking request handling
  • ๐Ÿš€ Fast JSON Serialization: Optimized response formatting
  • ๐Ÿ’พ Memory Efficient: Minimal resource footprint
  • ๐Ÿ“Š Request Batching: Efficient concurrent processing

๐Ÿ’ก Optimization Tips

# For high-throughput scenarios
# Increase worker processes
uvicorn main:app --workers 4 --host 0.0.0.0 --port 8000

# For memory optimization
# Use streaming for large texts
# Implement request queuing for traffic spikes

๐Ÿ”’ Security

๐Ÿ›ก๏ธ Security Features

FeatureImplementationStatus
API Key AuthenticationHeader-based validationโœ… Active
Input ValidationPydantic schema validationโœ… Active
Request Size Limits10MB maximum payloadโœ… Active
Error SanitizationNo sensitive data exposureโœ… Active
CORS ProtectionConfigurable originsโœ… Active

๐Ÿ” Authentication Methods

# Method 1: API Key Header (Recommended)
curl -H "X-Api-Key: your-api-key" http://localhost:8000/v1/summarize

# Method 2: Query Parameter (Not recommended for production)
curl "http://localhost:8000/v1/summarize?api_key=your-api-key"

๐Ÿšจ Security Best Practices

  • ๐Ÿ”‘ Rotate API keys regularly
  • ๐ŸŒ Use HTTPS in production
  • ๐Ÿ“Š Monitor API usage for anomalies
  • ๐Ÿ”’ Implement rate limiting for production
  • ๐Ÿ“ Log security events for audit trails

๐Ÿ› ๏ธ Development

๐Ÿš€ Getting Started

# Install development dependencies
pip install -r requirements-dev.txt

# Set up pre-commit hooks
pre-commit install

# Run development server with auto-reload
python main.py

# Format code
black main.py test_main.py
isort main.py test_main.py

# Lint code
flake8 main.py test_main.py
mypy main.py

๐Ÿ“ Project Structure

mcp-server/
โ”œโ”€โ”€ ๐Ÿ“„ main.py                 # Main application server
โ”œโ”€โ”€ ๐Ÿ“„ requirements.txt        # Production dependencies  
โ”œโ”€โ”€ ๐Ÿ“„ requirements-dev.txt    # Development dependencies
โ”œโ”€โ”€ ๐Ÿงช test_main.py           # Test suite
โ”œโ”€โ”€ ๐Ÿงช load_test.py           # Performance tests
โ”œโ”€โ”€ ๐Ÿณ Dockerfile             # Container configuration
โ”œโ”€โ”€ ๐Ÿณ docker-compose.yaml    # Multi-container setup
โ”œโ”€โ”€ ๐Ÿ“– README.md              # Project documentation
โ”œโ”€โ”€ ๐Ÿ“‹ mcp-api.yaml           # OpenAPI specification
โ”œโ”€โ”€ โš™๏ธ .env                    # Environment configuration
โ”œโ”€โ”€ ๐Ÿšซ .gitignore             # Git ignore rules
โ””โ”€โ”€ ๐Ÿ› ๏ธ Makefile               # Automation commands

๐Ÿ”ง Environment Variables

# Server Configuration
HOST=0.0.0.0
PORT=8000
DEBUG=false

# API Configuration
API_KEYS=test-api-key,production-key
MAX_TEXT_SIZE=10485760

# Performance
WORKERS=1
RELOAD=false

๐Ÿ“Š Monitoring

๐Ÿ“ˆ Health Checks

# Basic health check
curl http://localhost:8000/health

# Detailed health with metrics
curl http://localhost:8000/health/detailed

๐Ÿ“Š Metrics & Logging

  • ๐Ÿ“ Request Logging: All requests logged with UUID tracing
  • โฑ๏ธ Performance Metrics: Response times and throughput tracking
  • ๐Ÿšจ Error Monitoring: Structured error logging and alerting
  • ๐Ÿ’พ Resource Usage: Memory and CPU monitoring

๐Ÿ” Debugging

# Enable debug logging
export LOG_LEVEL=debug
python main.py

# View real-time logs
tail -f logs/mcp-server.log

# Test error scenarios
curl -X POST http://localhost:8000/v1/summarize \
  -H "X-Api-Key: invalid-key" \
  -d '{"requestId": "test", "text": "test"}'

๐Ÿค Contributing

We welcome contributions! Here's how to get started:

๐Ÿš€ Quick Contributing Guide

  1. ๐Ÿด Fork the repository
  2. ๐ŸŒฟ Create a feature branch (git checkout -b feature/amazing-feature)
  3. โœจ Make your changes
  4. โœ… Add tests for new functionality
  5. ๐Ÿงช Run the test suite (pytest)
  6. ๐Ÿ“ Commit your changes (git commit -m 'Add amazing feature')
  7. ๐Ÿš€ Push to the branch (git push origin feature/amazing-feature)
  8. ๐Ÿ“‹ Open a Pull Request

๐Ÿ“‹ Development Checklist

  • โœ… Tests pass (pytest test_main.py)
  • ๐ŸŽจ Code formatted (black, isort)
  • ๐Ÿ” Linting clean (flake8, mypy)
  • ๐Ÿ“– Documentation updated
  • ๐Ÿ”’ Security considerations addressed

๐Ÿ“Š API Reference

๐Ÿ“– Interactive API Documentation

Swagger UI ReDoc

๐Ÿ“‹ OpenAPI Specification

  • ๐Ÿ“„ YAML Format:
  • ๐ŸŒ JSON Endpoint: /openapi.json
  • ๐Ÿ“– Interactive Docs: /docs
  • ๐Ÿ“š Alternative Docs: /redoc

๐Ÿ“„ License

This project is licensed under the MIT License - see the file for details.

๐Ÿ“ MIT License Summary:

  • โœ… Commercial use
  • โœ… Modification
  • โœ… Distribution
  • โœ… Private use

๐ŸŒŸ Star History

Star History Chart


๐Ÿš€ Ready to transform your text processing workflow?

Get Started Now โ€ข View API Docs โ€ข Join Discord


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MCP Server - Professional Text Processing Made Simple