RivalSearchMCP

Advanced MCP server for web research, content discovery, social media analysis, and AI-powered research.

🆓 100% Free & Open Source — No API keys, no subscriptions, no rate limits. Just add one URL and go.

What It Does

RivalSearchMCP provides comprehensive tools for accessing web content, performing multi-engine searches across DuckDuckGo, Yahoo, and Wikipedia, analyzing websites, social media, news, GitHub repositories, and documents with OCR. It includes 10 specialized tools organized into key categories for comprehensive web research capabilities.

✅ Why It's Useful

• Access web content and perform searches with anti-detection measures
• Analyze website content and structure with intelligent crawling
• Conduct end-to-end research workflows with progress tracking
• Search social media platforms (Reddit, Hacker News, Dev.to, Product Hunt, Medium)
• Aggregate news from multiple sources with no authentication required
• Analyze documents (PDF, Word, Text, Images) with OCR support
• Search social media and news across 8 platforms simultaneously
• Integrate with AI assistants for enhanced web research

💡 Example Query

Once connected, try asking your AI assistant:

"Use RivalSearchMCP to research FastAPI vs Django. Search the web, check Reddit and Hacker News discussions, find recent news articles, search GitHub repositories, and analyze academic papers. Then use the research agent to generate a comprehensive comparison report."

📦 How to Get Started

RivalSearchMCP runs as a remote MCP server hosted on FastMCP. Just follow the steps below to install, and go.

Connect to Live Server

Or add this configuration manually:

For Cursor:

{
  "mcpServers": {
    "RivalSearchMCP": {
      "url": "https://RivalSearchMCP.fastmcp.app/mcp"
    }
  }
}

For Claude Desktop:

• Go to Settings → Add Remote Server
• Enter URL: https://RivalSearchMCP.fastmcp.app/mcp

For VS Code:

• Add the above JSON to your .vscode/mcp.json file

For Claude Code:

• Use the built-in MCP management: claude mcp add RivalSearchMCP --url https://RivalSearchMCP.fastmcp.app/mcp

Local Installation with FastMCP CLI

Prerequisites:

# Install UV (modern Python package manager)
curl -LsSf https://astral.sh/uv/install.sh | sh

# Install FastMCP CLI (optional but recommended)
uv tool install fastmcp

Method 1: One-Command Install (Easiest)

# Clone repository
git clone https://github.com/damionrashford/RivalSearchMCP.git
cd RivalSearchMCP

# Install directly to your MCP client:
fastmcp install claude-desktop server.py   # For Claude Desktop
fastmcp install cursor server.py           # For Cursor
fastmcp install claude-code server.py      # For Claude Code

Method 2: Quick Run (No Installation)

git clone https://github.com/damionrashford/RivalSearchMCP.git
cd RivalSearchMCP

# Run directly with FastMCP CLI
fastmcp run server.py  # Auto-detects entrypoint, uses STDIO

# Or run in HTTP mode for testing
fastmcp run server.py --transport http --port 8000

Method 3: Development with Inspector

# Run with MCP Inspector for testing
fastmcp dev server.py

Method 4: Manual UV Setup

git clone https://github.com/damionrashford/RivalSearchMCP.git
cd RivalSearchMCP
uv sync

# Add to Claude Desktop or Cursor config:
{
  "RivalSearchMCP": {
    "command": "uv",
    "args": [
      "--directory",
      "/full/path/to/RivalSearchMCP",
      "run",
      "python",
      "server.py"
    ]
  }
}

🛠 Available Tools (10 Total)

Search & Discovery (5 tools)

• web_search — Multi-engine search across DuckDuckGo, Yahoo, and Wikipedia with intelligent fallbacks
• social_search — Search Reddit, Hacker News, Dev.to, Product Hunt, and Medium (NO AUTH)
• news_aggregation — Aggregate news from Google News, DuckDuckGo News, and Yahoo News (NO AUTH)
• github_search — Search GitHub repositories with 60/hour rate limiting (NO AUTH)
• map_website — Intelligent website exploration with research, documentation, and mapping modes

Content Analysis (3 tools)

• content_operations — Consolidated tool for retrieving, streaming, analyzing, and extracting content from URLs
• research_topic — End-to-end research workflow for comprehensive topic analysis
• document_analysis — Extract text from PDF, Word, Text files, and Images with EasyOCR (NO AUTH, 50MB limit)

Research & Scientific (2 tools)

• scientific_research — Academic paper search and dataset discovery across arXiv, Semantic Scholar (NO AUTH)
• research_agent — AI research agent with autonomous tool calling using OpenRouter (7 tools available)

⚡ Key Features

• Multi-Engine Search: 3 search engines (DuckDuckGo, Yahoo, Wikipedia) with automatic fallbacks
• Social Media Research: Search across 5 platforms (Reddit, Hacker News, Dev.to, Product Hunt, Medium)
• News Aggregation: 3 news sources (Google News, DuckDuckGo News, Yahoo News)
• GitHub Integration: Repository search with built-in rate limiting
• Document Analysis: PDF, Word, Text, and Images with EasyOCR (zero-install, auto-downloads models)
• AI Research Agent: Autonomous research agent that uses 7 tools and generates 4000+ character reports
• Content Processing: Advanced content extraction and analysis with workflow hints
• Scientific Discovery: Academic paper and dataset search across arXiv and Semantic Scholar
• Zero Authentication: All 10 tools work without any API keys or authentication

💬 FAQ

Is RivalSearchMCP really free?

Yes! RivalSearchMCP is 100% free and open source under the MIT License. There are no API costs, no subscriptions, and no rate limits. You can use the hosted server or run it locally.

Do I need API keys?

No. RivalSearchMCP works completely without any API keys, authentication, or configuration. Just add the URL and use all 10 tools immediately.

What MCP clients are supported?

RivalSearchMCP works with any MCP-compatible client including Claude Desktop, Cursor, VS Code, and Claude Code.

Can I self-host this?

Absolutely! Clone the repo, install dependencies, and run python server.py. Full instructions are in the Getting Started section above.

📚 Documentation

For detailed guides and examples, visit the Full Documentation.

🤝 Contributing

Contributions are welcome! Whether it's fixing bugs, adding new research tools, or improving documentation, your help is appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

💡 Issues, Feedback & Support

Found a bug, have a feature request, or want to share how you're using RivalSearchMCP? We'd love to hear from you!

• Report a bug — Help us improve by reporting issues
• Request a feature — Suggest new capabilities you'd find useful
• Share your use case — Tell us how you're using RivalSearchMCP

👉 Open an Issue

Attribution & License

This is an open source project under the MIT License. If you use RivalSearchMCP, please credit it by linking back to RivalSearchMCP. See LICENSE file for details.

⭐ Like this project? Give it a star!

If you find RivalSearchMCP useful, please consider giving it a star. It helps others discover the project and motivates continued development!