rival-search-mcp-skills

RivalSearchMCP

Deterministic research MCP server — web + social + academic + news + code + docs, all in one place. No API keys, no in-server LLM, structured outputs for agent chaining.

🆓 100% Free & Open Source — No API keys or subscriptions for core tools. The hosted server includes fair-use rate limiting.

What It Does

RivalSearchMCP is a FastMCP 3.x server exposing 9 specialized tools that search, fetch, score, and compare information across:

• 5 web search engines (DuckDuckGo, Bing, Yahoo, Mojeek, Wikipedia) — concurrent, deduplicated, with TLS-fingerprint-safe fetches via Scrapling
• 9 social platforms (Reddit, Hacker News, Stack Overflow, Dev.to, Medium, Product Hunt, Bluesky, Lobste.rs, Lemmy) — no authentication
• 5 news sources (Google News, Bing News, The Guardian, GDELT, DuckDuckGo News) — with time-range filtering
• 5 academic databases (OpenAlex, CrossRef, arXiv, PubMed, Europe PMC) + 4 dataset hubs (Kaggle, HuggingFace, Dataverse, Zenodo)
• GitHub repositories with built-in rate limiting
• Documents (PDF, Word, text, images) with OCR for images
• Website traversal with research, docs, and mapping modes

No LLM runs inside the server. Every tool returns deterministic, auditable output — the caller's model does the synthesis. Tools that benefit from structured output (content_operations score, find_conflicts) return ToolResult with both a human-readable markdown rendering and a parseable structuredContent dict, so agents can chain tool outputs without regex-parsing prose.

✅ Why It's Useful

• One connection, nine tools — no need to wire up separate MCP servers per source
• Auto-quality scoring — every result carries a tier/freshness/corroboration/citation score (0-100) and every multi-result response carries an aggregate confidence signal
• Conflict detection — content_operations find_conflicts surfaces numeric, date, and polarity disagreements across sources as a first-class signal instead of averaging them away
• Entity profiles — research_topic(mode="entity") fans out to 8 sources in parallel and returns a unified report with confidence
• Production hygiene — per-tool timeouts, rate limiting (100 req/min/session), response-size caps, error masking, middleware-level observability

💡 Example Query

Once connected, try asking your AI assistant:

"Use RivalSearchMCP to research FastAPI vs Django. Run research_topic on both, aggregate recent news, check Reddit and Hacker News discussions, search GitHub for activity, look for academic papers, score the top sources, and flag any conflicts between them."

📦 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