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, HuggingFace, Dataverse, Zenodo).

Plugins

RivalSearchMCP ships as an installable plugin for both Claude Code and OpenAI Codex. The plugin registers the hosted MCP server at https://RivalSearchMCP.fastmcp.app/mcp, so users do not need to clone this repo or run a local server.

The GitHub repo is the plugin marketplace. Add the marketplace once, then install either or both plugins:

• rival-search-mcp registers the hosted MCP server and exposes the 9 tools.
• rival-search-mcp-skills installs the standalone agent skill, reference docs, and CLI helpers.

Claude Code

From a terminal:

# 1. Add this repo as a marketplace
claude plugin marketplace add damionrashford/RivalSearchMCP --scope user

# 2. Install the plugin
claude plugin install rival-search-mcp@rivalsearchmcp --scope user

# Optional: install the skill-only plugin
claude plugin install rival-search-mcp-skills@rivalsearchmcp --scope user

Inside Claude Code, the same flow is available with slash commands:

/plugin marketplace add damionrashford/RivalSearchMCP
/plugin install rival-search-mcp@rivalsearchmcp
/plugin install rival-search-mcp-skills@rivalsearchmcp

For a team/project install, use --scope project instead of --scope user, or add the marketplace to .claude/settings.json:

{
  "extraKnownMarketplaces": {
    "rivalsearchmcp": {
      "source": {
        "source": "github",
        "repo": "damionrashford/RivalSearchMCP"
      }
    }
  }
}

Then install one or both plugins with /plugin install rival-search-mcp@rivalsearchmcp and /plugin install rival-search-mcp-skills@rivalsearchmcp.

Once installed, Claude Code exposes the MCP tools as mcp__RivalSearchMCP__*. Example prompts:

Use RivalSearchMCP to search recent news about open source AI agents.
Use RivalSearchMCP to compare FastAPI and Django across web, GitHub, and academic sources.
Use RivalSearchMCP to retrieve this URL, score the source, and identify conflicting claims.