rival-search-mcp

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

# 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 (9 Total)

Every tool carries ToolAnnotations (readOnlyHint, openWorldHint, destructiveHint, idempotentHint) so MCP clients like Claude and ChatGPT can skip confirmation prompts where safe. Every tool has a timeout= ceiling so a hung source can't stall the client.

Search & Discovery (5 tools)

• web_search — concurrent multi-engine search across DuckDuckGo, Bing, Yahoo, Mojeek, and Wikipedia. Scrapling-backed TLS fingerprinting bypasses Cloudflare/Akamai fronting. Per-engine failures don't block the others.
• social_search — 9 platforms: Reddit, Hacker News, Stack Overflow, Dev.to, Medium, Product Hunt, Bluesky, Lobste.rs, Lemmy. No authentication.
• news_aggregation — 5 sources: Google News, Bing News, The Guardian, GDELT, DuckDuckGo News. Accepts time_range (day/week/month/anytime).
• github_search — repository search with built-in rate limiting (60/hr unauthenticated), optional README inclusion.
• map_website — traverse a site in research, docs, or map mode; returns per-page quality scores and an aggregate confidence signal.

Content Analysis (3 tools)

• content_operations — one tool, six operations: retrieve, stream, analyze, extract, score, find_conflicts.
  • score rates URLs on tier / freshness / corroboration / citations (0-100) and returns both markdown + structured JSON.
  • find_conflicts compares 2-10 sources for numeric / date / polarity disagreements with confidence weights.
• research_topic — two modes: topic (search + fetch + relevance-ranked key findings) and entity (unified cross-source profile of a named entity, fanning out to web / news / GitHub / social / academic in parallel).
• document_analysis — extract text from PDF, Word, plain text, and images. Images use EasyOCR (lazy-loaded; no setup). 50 MB cap.

Research Workflow (1 tool)

• scientific_research — academic paper and dataset search. 5 paper providers (OpenAlex, CrossRef, arXiv, PubMed, Europe PMC) and 4 dataset hubs (Kaggle, Hugging