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

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.

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!

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.

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)

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.

┌─────────────────────────────────────────────────────────────┐
│                        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)

• 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

• Python 3.8+
• ffmpeg (for audio/video processing)

# 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

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)

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"
    }
  }
}

• 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

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

• 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

• 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)

• analyze_query: Understand query intent and context needs

• 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

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

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..."

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!

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)

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!

• 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

• 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

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.

Here are real examples generated by the MCP server:

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

Topic: New York Mayor (Complete Talking Head)

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)

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

✅ 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

✅ 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%

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

MIT License - Feel free to use and modify.

• 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.