damionrashford/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:**
**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:**
**Method 1: One-Command Install (Easiest)**
**Method 2: Quick Run (No Installation)**
**Method 3: Development with Inspector**
**Method 4: Manual UV Setup**
🛠 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.