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Model Context Protocol

RSS Editorial Server Implementation

A deep dive into MCP-powered content aggregation and AI editorial generation

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What is Model Context Protocol?

Standardized communication protocol for AI assistants
Enables interaction with external tools and data sources
JSON-RPC 2.0 based messaging
Protocol version: 2024-11-05

AI Assistant

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MCP Server

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External Data

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RSS Editorial Server Overview

A PHP implementation that demonstrates MCP's power through:

📰 RSS Management

Add, list, and fetch content from multiple RSS sources

📊 Topic Analysis

Extract and analyze trending topics from aggregated feeds

✍️ Editorial Generation

Create AI-powered editorial pieces based on current news

📁 Resource Exposure

Make feed data accessible as readable resources

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Core MCP Components

{
    "protocolVersion": "2024-11-05",
    "capabilities": {
        "tools": [],        // Custom functions
        "resources": ["read"],  // Resource access
        "prompts": []       // Prompt templates
    },
    "serverInfo": {
        "name": "rss-editorial-mcp",
        "version": "1.0.0"
    }
}

The server announces its capabilities during initialization

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Communication Architecture

React PHP for Asynchronous I/O

$this->input = new ReadableResourceStream(STDIN);
$this->output = new WritableResourceStream(STDOUT);

JSON-RPC 2.0 messaging over stdin/stdout
Event-driven architecture
Non-blocking I/O operations
Handles multiple concurrent requests

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MCP Tools Exposed

➕ add_feed

Register new RSS feeds
Parameters: URL, name

📈 analyze_topics

Analyze trending topics
Parameters: limit (items per feed)

📝 generate_editorial

Create editorial content
Parameters: topic, style, length

📋 list_feeds

Show configured feeds
No parameters required

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Topic Analysis Implementation

private function extractKeywords($text) {
    // Natural language processing
    $text = strtolower($text);
    $text = preg_replace('/[^a-z0-9\s]/', '', $text);

    // Filter stop words
    $stopWords = ['the', 'is', 'at', 'which', ...];

    // Extract meaningful keywords
    return array_unique($keywords);
}

Simple but effective NLP for identifying trending topics across feeds

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Claude AI Integration

Intelligent Editorial Generation

Configurable models: claude-3-5-sonnet or claude-3-5-haiku
Context-aware prompts based on editorial style
Graceful fallback to template-based generation

// Editorial styles supported
- Analytical: Balanced, fact-based analysis
- Opinion: Persuasive arguments with clear stance
- Summary: Comprehensive overview for general audience

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MCP Resource System

Expose feed data through custom URI schemes:

// Resource URIs
feed://techcrunch     → Access TechCrunch feed content
feed://reuters        → Access Reuters feed content
editorial://drafts    → Access generated editorials

// Resource metadata
{
    "uri": "feed://techcrunch",
    "name": "TechCrunch",
    "mimeType": "application/rss+xml"
}

Resources can be read by AI assistants for further processing

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MCP Protocol Flow

1. Client connects → Server sends capabilities

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2. Client initializes → Server confirms protocol

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3. Client requests tools → Server describes available tools

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4. Client calls tool → Server executes and returns results

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Key Benefits

🔧 Modularity

Each tool is self-contained and focused on a specific task

🔌 Extensibility

Easy to add new feeds, tools, or editorial styles

🤖 AI-Enhanced

Leverages Claude for sophisticated content generation

⚡ Real-time

Processes current news as it arrives with smart caching

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🎯

Conclusion

MCP enables powerful AI integrations

Standardized protocol for tool integration
Bridges external data with AI capabilities
Clean separation of concerns
Compatible with any MCP-compliant assistant

The RSS Editorial Server demonstrates MCP's potential
for creating intelligent, data-driven applications

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