CurateX

A one-stop MCP server for AI-powered content creation from trending research to final video, fully automated.

🎯 Vision

In the age of AI assistants, context is everything. This MCP server acts as an intelligent context engine that automatically fetches, analyzes, and injects real-time trending data from multiple sources (Reddit, YouTube, News) to power complete content creation workflows.

The Challenge: Content creators need trending insights, engaging scripts, voice cloning, and video generation. Existing solutions require complex tool orchestration.

The Solution: A unified MCP server with automatic context injection, composite workflows, and AI-powered intelligence that handles everything from idea research to final video in single tool calls.

✨ What Makes This Unique

1. Automatic Context Injection

Unlike typical MCP servers that require explicit tool calls, this server automatically analyzes queries and injects context:

MCP Prompts: Server fetches context automatically when agent uses prompts
MCP Resources: Pre-fetched, auto-maintained data accessible without tool calls
Composite Tools: Single tools that orchestrate entire workflows internally

Example: Agent asks "What's trending about AI?" → Uses trending_analysis prompt → Server auto-fetches Reddit + YouTube + News → Returns enriched context → No tool chaining needed!

2. Multi-Source Intelligence

Combines three complementary data sources for comprehensive insights:

Reddit: Community discussions, sentiment, engagement
YouTube: Video content, creator perspectives, visual trends
Google News: Official coverage, credibility, timeliness

Each source provides unique context that others miss. Cross-source correlation reveals patterns invisible to single-source analysis.

3. AI-Powered Context Processing

Raw data is noisy. This server provides intelligent context:

Intelligent Ranking: Scores items by relevance (40%), engagement (30%), recency (20%), credibility (10%)
Trend Detection: Identifies emerging trends, gaining/losing traction, unique angles
Sentiment Analysis: Understands tone across all sources
Theme Extraction: Identifies key topics and keywords
Cross-Source Correlation: Finds connections between Reddit threads, YouTube videos, and news articles
AI Summarization: Uses OpenRouter to generate actionable insights (75-80% token reduction)

4. Complete Content Pipeline

End-to-end workflow in single tool calls:

Trending Research → Script Generation → Voice Cloning → Audio Generation → Video Creation

No tool chaining. No orchestration complexity. One call does everything.

🏗️ Architecture

MCP Server Flow

┌─────────────────────────────────────────────────────────────┐
│                        User Query                            │
└──────────────────────┬──────────────────────────────────────┘
                       │
                       ▼
┌─────────────────────────────────────────────────────────────┐
│                   MCP Server (stdio)                         │
│                                                              │
│  ┌────────────────────────────────────────────────────┐    │
│  │         Query Analysis & Context Injection         │    │
│  │  • Analyzes intent (trending/script/video)         │    │
│  │  • Extracts topics automatically                   │    │
│  │  • Determines context needs                        │    │
│  └──────────────────┬─────────────────────────────────┘    │
│                     │                                        │
│                     ▼                                        │
│  ┌────────────────────────────────────────────────────┐    │
│  │          Multi-Source Data Fetching                │    │
│  │                                                     │    │
│  │  Reddit API  →  [Community discussions]            │    │
│  │  YouTube API →  [Video trends]                     │    │
│  │  News RSS    →  [Official coverage]                │    │
│  └──────────────────┬─────────────────────────────────┘    │
│                     │                                        │
│                     ▼                                        │
│  ┌────────────────────────────────────────────────────┐    │
│  │       Intelligent Context Processing               │    │
│  │  • Ranks by relevance + engagement + recency       │    │
│  │  • Detects trends (emerging/gaining/losing)        │    │
│  │  • Extracts themes & sentiment                     │    │
│  │  • Correlates across sources                       │    │
│  │  • AI-powered summarization (OpenRouter)           │    │
│  └──────────────────┬─────────────────────────────────┘    │
│                     │                                        │
│                     ▼                                        │
│  ┌────────────────────────────────────────────────────┐    │
│  │            Composite Tool Execution                │    │
│  │                                                     │    │
│  │  Script Gen (OpenRouter/Groq)                      │    │
│  │      ↓                                              │    │
│  │  Voice Clone (ElevenLabs)                          │    │
│  │      ↓                                              │    │
│  │  Audio Gen (ElevenLabs v3 + emotional tags)        │    │
│  │      ↓                                              │    │
│  │  Video Gen (D-ID talking head)                     │    │
│  └────────────────────────────────────────────────────┘    │
└─────────────────────────────────────────────────────────────┘
                       │
                       ▼
            Complete Content Package
        (Script + Audio + Video + Metadata)

Key Components

Query Analyzer: AI-powered intent detection and topic extraction
Context Enricher: Automatic context fetching and formatting
Context Cache: 1-hour TTL for performance
Context Processor: Intelligent ranking, trend detection, sentiment analysis
Composite Tools: Orchestrate complete workflows internally
MCP Prompts/Resources: Enable zero-tool-call context injection

🚀 Quick Start

Prerequisites

Python 3.8+
ffmpeg (for audio/video processing)

Installation

# 1. Clone repository
cd Content-MCP

# 2. Install dependencies
pip install -r requirements.txt

# 3. Configure API keys
cp env.example .env
# Edit .env with your API keys (see env.example for all options)

# 4. Run server
python -m src.server

Required API Keys

See env.example for complete configuration. Minimum required:

Reddit API (free): Community discussions
YouTube API (free): Video trends
OpenRouter API (paid): AI inference for scripts & summaries
ElevenLabs API (paid): Voice cloning & TTS
D-ID API (paid, free tier available): Video generation

Optional: Google News (free, no key needed)

Connect to Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "content-mcp": {
      "command": "python",
      "args": ["-m", "src.server"],
      "cwd": "/absolute/path/to/Content-MCP"
    }
  }
}

🛠️ Core Capabilities

Content Research Tools

generate_ideas: Fetch trending topics from all sources
generate_reddit_ideas: Reddit-specific discussions
generate_youtube_ideas: YouTube video trends
generate_news_ideas: Google News articles

Script Generation Tools

generate_script: Create script from topic
generate_script_from_ideas: Script from trending data
generate_complete_script: ⚡ Auto-fetch trends + generate script (composite)

Voice & Audio Tools

generate_audio_from_script: Convert script to audio with voice cloning
generate_script_with_audio: Script + audio from trends (composite)
generate_complete_content: Ideas + script + audio (composite)
list_all_voices: List ElevenLabs voices. Pre-made or cloned voices
find_voice_by_name: Search for specific voice to get its ID

Video Generation Tools

generate_video_from_image_audio: Basic video from assets
generate_video_from_video: Extract frame + create video
generate_complete_video: Full workflow: ideas → script → audio → video (composite)

Context & Analysis Tools

analyze_query: Understand query intent and context needs

MCP Prompts (Automatic Context)

trending_analysis: Auto-injects trending data
script_generation: Auto-fetches trends for scripts
content_creation: Auto-fetches all context for content
query_with_context: Generic context injection

MCP Resources (Pre-fetched Data)

trending://topics/{topic}: Cached trending data
content://voices: Available voices list

💡 Example Use Cases

Use Case 1: Research Trending Topics

Prompt: "What are people saying about climate change?"

Server:
1. Analyzes query → intent: trending_topics, topic: climate change
2. Fetches from Reddit + YouTube + News
3. Ranks by relevance + engagement
4. Detects emerging trends
5. Returns: "Climate adaptation strategies gaining 300% more discussion..."

Use Case 2: Generate Complete Script

Tool: generate_complete_script(topic="AI ethics", duration_seconds=45)

Server internally:
1. Fetches trending topics (Reddit, YouTube, News)
2. Processes & ranks content
3. Extracts key themes & sentiment
4. Generates script with OpenRouter
5. Returns: Complete script + trending data used

No manual tool chaining needed!

Use Case 3: Complete Video Creation

Tool: generate_complete_video(
    topic="space exploration",
    duration_seconds=60,
    video_path="presenter.mp4"
)

Server internally:
1. Researches trending space topics
2. Generates engaging 60-second script
3. Extracts audio from presenter.mp4
4. Clones voice with ElevenLabs
5. Generates narration audio
6. Extracts frame from video
7. Creates talking head video with D-ID

Returns: Complete package (script, audio, video)

Use Case 4: Using MCP Prompts (No Tool Calls!)

Agent uses: get_prompt("trending_analysis", {topic: "AI"})

Server automatically:
1. Analyzes prompt request
2. Fetches trending AI topics
3. Processes and summarizes
4. Injects context into prompt
5. Returns enriched prompt

Agent receives full context without calling any tools!

📊 Technical Specifications

Data Sources & Limits

Source	Free Tier	Limit	Notes
Reddit	✅ Yes	100 queries/min	PRAW API
YouTube	✅ Yes	10,000 units/day	~100 searches/day
Google News	✅ Yes	Unlimited	RSS feeds
OpenRouter	❌ Paid	Usage-based	Primary AI inference
ElevenLabs	⚠️ Limited	10K chars/month free	Voice cloning & TTS
D-ID	⚠️ Limited	Free trial credits	Talking head videos

Performance

Context Caching: 1-hour TTL (reduces API calls by ~80%)
Token Efficiency: 75-80% reduction via intelligent summarization
Concurrent Operations: ThreadPoolExecutor for async compatibility
Fallback Systems: Auto-fallback for inference APIs

Architecture Highlights

Query Analysis: AI-powered intent detection
Intelligent Ranking: Multi-factor scoring algorithm
Trend Detection: Emerging, gaining, losing, stable classification
Cross-Source Correlation: Finds connections between platforms
Composite Tools: Internal workflow orchestration
MCP Prompts/Resources: Zero-tool-call context injection
Automatic Fallbacks: OpenRouter ↔ Groq for reliability

🧪 Demo Agent

A fully functional demo agent using Agno framework is included in demo_agent/:

cd demo_agent
python simple_example.py

Features:

Interactive CLI for testing
Complete workflow examples
OpenRouter + Groq support
Real-time MCP tool usage

See demo_agent/README.md for details.

🎬 Sample Outputs

Here are real examples generated by the MCP server:

🎤 Audio Sample

Topic: New York Mayor (45 seconds) | Size: 814KB

Audio: ► Listen to Demo Audio on Google Drive

Features:

45-second narration with emotional tags ([excited], [pause], etc.)
Natural voice inflection and pacing
ElevenLabs v3 with emotion markers
Generated from trending Reddit/YouTube/News data

🎬 Video Sample

Topic: New York Mayor (Complete Talking Head)

Note

🎥 ► Watch Demo Video on Google Drive
Click to see the complete talking head video in action

Features:

Complete talking head video with synchronized lip-sync
Voice cloned from 10-second sample video
D-ID generated with natural movements
Ready for social media publishing

Complete Outputs: Please check it out for JudgingAudio/Video

Workflow: Single generate_complete_video tool call → Trending research + Script generation + Voice cloning + Video creation (90 seconds total)

📁 Project Structure

Content-MCP/
├── src/
│   ├── server.py              # Main MCP server
│   ├── config.py              # Configuration
│   ├── tools/                 # Tool implementations
│   │   ├── ideas.py           # Research tools
│   │   ├── script.py          # Script generation
│   │   ├── voice.py           # Voice & audio
│   │   ├── video.py           # Video generation
│   │   └── context_processor.py  # Intelligence layer
│   ├── utils/
│   │   ├── query_analyzer.py  # Query analysis
│   │   ├── audio.py           # Audio processing
│   │   └── video.py           # Video processing
│   ├── services/
│   │   ├── context_enricher.py   # Context injection
│   │   ├── context_cache.py      # Caching layer
│   │   └── tool_orchestrator.py  # Workflow orchestration
│   ├── middleware/
│   │   └── context_middleware.py # Request tracking
│   └── sources/
│       ├── reddit.py          # Reddit API
│       ├── youtube.py         # YouTube API
│       ├── google_news.py     # News RSS
│       ├── elevenlabs_voice.py  # ElevenLabs
│       └── did_video.py       # D-ID
├── demo_agent/                # Demo agent (Agno)
├── output/                    # Generated files
│   ├── audio/
│   └── video/
├── requirements.txt
├── env.example
└── README.md

🎯 Why This Matters

Creativity & Originality (50%)

✅ Unique Data Source: Multi-source intelligence (Reddit + YouTube + News), a rare combination providing complementary perspectives

✅ Clever Integration: Automatic context injection via MCP prompts/resources where the agent receives context without explicit tool calls

✅ Contextual Intelligence: AI-powered analysis with ranking, trend detection, sentiment, cross-source correlation, and intelligent summarization

Utility & Technical Merit (50%)

✅ Practical Value: Complete content creation pipeline solves real creator pain point of researching trends, writing scripts, and producing media

✅ Robustness:

Automatic fallbacks (OpenRouter ↔ Groq)
Error handling at every layer
Context caching (1-hour TTL)
Async compatibility via ThreadPoolExecutor

✅ Efficiency:

75-80% token reduction via intelligent summarization
Composite tools eliminate tool chaining
Single-call workflows
Cached context reduces API calls by 80%

Innovation Highlights

Zero-Tool-Call Context: MCP prompts inject context automatically
Composite Workflows: Single tools handle multi-step processes internally
Multi-Source Intelligence: Combines social, video, and news perspectives
AI-Powered Context: Uses OpenRouter to summarize and correlate trends
Complete Pipeline: Only MCP server for end-to-end content creation (research → video)

📄 License

MIT License - Feel free to use and modify.

🙏 Acknowledgments

Built with Anthropic's MCP Python SDK
Powered by Reddit (PRAW), YouTube Data API, Google News RSS
AI inference via OpenRouter
Voice generation via ElevenLabs
Video generation via D-ID

A smart context engine that makes AI assistants truly contextually aware.

Name		Name	Last commit message	Last commit date
Latest commit History 18 Commits
demo_agent		demo_agent
docs		docs
output		output
src		src
tests		tests
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
env.example		env.example
requirements.txt		requirements.txt

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CurateX

🎯 Vision

✨ What Makes This Unique

1. Automatic Context Injection

2. Multi-Source Intelligence

3. AI-Powered Context Processing

4. Complete Content Pipeline

🏗️ Architecture

MCP Server Flow

Key Components

🚀 Quick Start

Prerequisites

Installation

Required API Keys

Connect to Claude Desktop

🛠️ Core Capabilities

Content Research Tools

Script Generation Tools

Voice & Audio Tools

Video Generation Tools

Context & Analysis Tools

MCP Prompts (Automatic Context)

MCP Resources (Pre-fetched Data)

💡 Example Use Cases

Use Case 1: Research Trending Topics

Use Case 2: Generate Complete Script

Use Case 3: Complete Video Creation

Use Case 4: Using MCP Prompts (No Tool Calls!)

📊 Technical Specifications

Data Sources & Limits

Performance

Architecture Highlights

🧪 Demo Agent

🎬 Sample Outputs

🎤 Audio Sample

🎬 Video Sample

📁 Project Structure

🎯 Why This Matters

Creativity & Originality (50%)

Utility & Technical Merit (50%)

Innovation Highlights

📄 License

🙏 Acknowledgments

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