{"id":52536847,"url":"https://github.com/damionrashford/rivalsearchmcp","last_synced_at":"2026-08-27T23:38:51.364Z","repository":{"id":307917262,"uuid":"1031075913","full_name":"damionrashford/RivalSearchMCP","owner":"damionrashford","description":"Deterministic research MCP server on FastMCP 3 — 5-engine web search, 9-platform social search, 6 academic DBs, news aggregation, entity profiles, conflict detection, document analysis. No API keys. No in-server LLM. 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RivalSearchMCP\n\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg?style=for-the-badge)](LICENSE)\n![MCP Server](https://img.shields.io/badge/MCP-Server-blue?style=for-the-badge)\n![Python](https://img.shields.io/badge/Python-3.10+-3776AB?logo=python\u0026logoColor=white\u0026style=for-the-badge)\n![FastMCP](https://img.shields.io/badge/FastMCP-3.2+-green?style=for-the-badge)\n[![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue?logo=linkedin\u0026style=for-the-badge)](https://www.linkedin.com/in/damion-rashford)\n\n![GitHub Stars](https://img.shields.io/github/stars/damionrashford/RivalSearchMCP?style=social)\n![GitHub Forks](https://img.shields.io/github/forks/damionrashford/RivalSearchMCP?style=social)\n![GitHub Issues](https://img.shields.io/github/issues/damionrashford/RivalSearchMCP?style=social)\n![Last Commit](https://img.shields.io/github/last-commit/damionrashford/RivalSearchMCP?style=social)\n![Visitor Count](https://visitor-badge.laobi.icu/badge?page_id=damionrashford.RivalSearchMCP)\n\n**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.**\n\n\u003e 🆓 **100% Free \u0026 Open Source** — No API keys or subscriptions for core tools. The hosted server includes fair-use rate limiting.\n\n## What It Does\n\nRivalSearchMCP is a FastMCP 3.x server exposing **9 specialized tools** that search, fetch, score, and compare information across:\n\n- **5 web search engines** (DuckDuckGo, Bing, Yahoo, Mojeek, Wikipedia) — concurrent, deduplicated, with TLS-fingerprint-safe fetches via Scrapling\n- **9 social platforms** (Reddit, Hacker News, Stack Overflow, Dev.to, Medium, Product Hunt, Bluesky, Lobste.rs, Lemmy) — no authentication\n- **5 news sources** (Google News, Bing News, The Guardian, GDELT, DuckDuckGo News) — with time-range filtering\n- **5 academic databases** (OpenAlex, CrossRef, arXiv, PubMed, Europe PMC) + **4 dataset hubs** (Kaggle, HuggingFace, Dataverse, Zenodo)\n- **GitHub repositories** with built-in rate limiting\n- **Documents** (PDF, Word, text, images) with OCR for images\n- **Website traversal** with research, docs, and mapping modes\n\n**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.\n\n## ✅ Why It's Useful\n\n- **One connection, nine tools** — no need to wire up separate MCP servers per source\n- **Auto-quality scoring** — every result carries a tier/freshness/corroboration/citation score (0-100) and every multi-result response carries an aggregate confidence signal\n- **Conflict detection** — `content_operations find_conflicts` surfaces numeric, date, and polarity disagreements across sources as a first-class signal instead of averaging them away\n- **Entity profiles** — `research_topic(mode=\"entity\")` fans out to 8 sources in parallel and returns a unified report with confidence\n- **Production hygiene** — per-tool timeouts, rate limiting (100 req/min/session), response-size caps, error masking, middleware-level observability\n\n## 💡 Example Query\n\nOnce connected, try asking your AI assistant:\n\n\u003e \"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.\"\n\n## 📦 How to Get Started\n\nRivalSearchMCP runs as a **remote MCP server** hosted on FastMCP. Just follow the steps below to install, and go.\n\n### Connect to Live Server\n\n[![Install MCP Server](https://cursor.com/deeplink/mcp-install-dark.svg)](https://cursor.com/en-US/install-mcp?name=RivalSearchMCP\u0026config=eyJ1cmwiOiJodHRwczovL1JpdmFsU2VhcmNoTUNQLmZhc3RtY3AuYXBwL21jcCJ9)\n\nOr add this configuration manually:\n\n**For Cursor:**\n```json\n{\n  \"mcpServers\": {\n    \"RivalSearchMCP\": {\n      \"url\": \"https://RivalSearchMCP.fastmcp.app/mcp\"\n    }\n  }\n}\n```\n\n**For Claude Desktop:**\n- Go to Settings → Add Remote Server\n- Enter URL: `https://RivalSearchMCP.fastmcp.app/mcp`\n\n**For VS Code:**\n- Add the above JSON to your `.vscode/mcp.json` file\n\n**For Claude Code:**\n- Use the built-in MCP management: `claude mcp add RivalSearchMCP --url https://RivalSearchMCP.fastmcp.app/mcp`\n\n### Local Installation with FastMCP CLI\n\n**Prerequisites:**\n```bash\n# Install UV (modern Python package manager)\ncurl -LsSf https://astral.sh/uv/install.sh | sh\n\n# Install FastMCP CLI (optional but recommended)\nuv tool install fastmcp\n```\n\n**Method 1: One-Command Install (Easiest)**\n```bash\n# Clone repository\ngit clone https://github.com/damionrashford/RivalSearchMCP.git\ncd RivalSearchMCP\n\n# Install directly to your MCP client:\nfastmcp install claude-desktop server.py   # For Claude Desktop\nfastmcp install cursor server.py           # For Cursor\nfastmcp install claude-code server.py      # For Claude Code\n```\n\n**Method 2: Quick Run (No Installation)**\n```bash\ngit clone https://github.com/damionrashford/RivalSearchMCP.git\ncd RivalSearchMCP\n\n# Run directly with FastMCP CLI\nfastmcp run server.py  # Auto-detects entrypoint, uses STDIO\n\n# Or run in HTTP mode for testing\nfastmcp run server.py --transport http --port 8000\n```\n\n**Method 3: Development with Inspector**\n```bash\n# Run with MCP Inspector for testing\nfastmcp dev server.py\n```\n\n**Method 4: Manual UV Setup**\n```bash\ngit clone https://github.com/damionrashford/RivalSearchMCP.git\ncd RivalSearchMCP\nuv sync\n\n# Add to Claude Desktop or Cursor config:\n{\n  \"RivalSearchMCP\": {\n    \"command\": \"uv\",\n    \"args\": [\n      \"--directory\",\n      \"/full/path/to/RivalSearchMCP\",\n      \"run\",\n      \"python\",\n      \"server.py\"\n    ]\n  }\n}\n```\n\n## 🛠 Available Tools (9 Total)\n\nEvery 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.\n\n### Search \u0026 Discovery (5 tools)\n- **`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.\n- **`social_search`** — 9 platforms: Reddit, Hacker News, Stack Overflow, Dev.to, Medium, Product Hunt, Bluesky, Lobste.rs, Lemmy. No authentication.\n- **`news_aggregation`** — 5 sources: Google News, Bing News, The Guardian, GDELT, DuckDuckGo News. Accepts `time_range` (day/week/month/anytime).\n- **`github_search`** — repository search with built-in rate limiting (60/hr unauthenticated), optional README inclusion.\n- **`map_website`** — traverse a site in `research`, `docs`, or `map` mode; returns per-page quality scores and an aggregate confidence signal.\n\n### Content Analysis (3 tools)\n- **`content_operations`** — one tool, six operations: `retrieve`, `stream`, `analyze`, `extract`, `score`, `find_conflicts`.\n  - `score` rates URLs on tier / freshness / corroboration / citations (0-100) and returns both markdown + structured JSON.\n  - `find_conflicts` compares 2-10 sources for numeric / date / polarity disagreements with confidence weights.\n- **`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).\n- **`document_analysis`** — extract text from PDF, Word, plain text, and images. Images use EasyOCR (lazy-loaded; no setup). 50 MB cap.\n\n### Research Workflow (1 tool)\n- **`scientific_research`** — academic paper and dataset search. 5 paper providers (OpenAlex, CrossRef, arXiv, PubMed, Europe PMC) and 4 dataset hubs (Kaggle, HuggingFace, Dataverse, Zenodo).\n\n## Plugins\n\nRivalSearchMCP 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.\n\nThe GitHub repo is the plugin marketplace. Add the marketplace once, then install either or both plugins:\n\n- `rival-search-mcp` registers the hosted MCP server and exposes the 9 tools.\n- `rival-search-mcp-skills` installs the standalone agent skill, reference docs, and CLI helpers.\n\n### Claude Code\n\nFrom a terminal:\n\n```bash\n# 1. Add this repo as a marketplace\nclaude plugin marketplace add damionrashford/RivalSearchMCP --scope user\n\n# 2. Install the plugin\nclaude plugin install rival-search-mcp@rivalsearchmcp --scope user\n\n# Optional: install the skill-only plugin\nclaude plugin install rival-search-mcp-skills@rivalsearchmcp --scope user\n```\n\nInside Claude Code, the same flow is available with slash commands:\n\n```text\n/plugin marketplace add damionrashford/RivalSearchMCP\n/plugin install rival-search-mcp@rivalsearchmcp\n/plugin install rival-search-mcp-skills@rivalsearchmcp\n```\n\nFor a team/project install, use `--scope project` instead of `--scope user`, or add the marketplace to `.claude/settings.json`:\n\n```json\n{\n  \"extraKnownMarketplaces\": {\n    \"rivalsearchmcp\": {\n      \"source\": {\n        \"source\": \"github\",\n        \"repo\": \"damionrashford/RivalSearchMCP\"\n      }\n    }\n  }\n}\n```\nThen install one or both plugins with `/plugin install rival-search-mcp@rivalsearchmcp` and `/plugin install rival-search-mcp-skills@rivalsearchmcp`.\n\nOnce installed, Claude Code exposes the MCP tools as `mcp__RivalSearchMCP__*`. Example prompts:\n\n```text\nUse RivalSearchMCP to search recent news about open source AI agents.\nUse RivalSearchMCP to compare FastAPI and Django across web, GitHub, and academic sources.\nUse RivalSearchMCP to retrieve this URL, score the source, and identify conflicting claims.\n```\n\n### OpenAI Codex\n\n```bash\n# 1. Add this repo as a marketplace\ncodex plugin marketplace add damionrashford/RivalSearchMCP --ref main\n\n# 2. Install the plugin\ncodex plugin add rival-search-mcp@rival-search-mcp-marketplace\n\n# Optional: install the skill-only plugin\ncodex plugin add rival-search-mcp-skills@rival-search-mcp-marketplace\n```\n\nOnce installed, start or refresh a Codex session and ask Codex to use RivalSearchMCP. Example prompts:\n\n```text\nUse RivalSearchMCP to run web_search for current MCP server comparisons.\nUse RivalSearchMCP to research this company across news, GitHub, and social sources.\nUse RivalSearchMCP to find scientific papers about retrieval augmented generation.\n```\n\nIf you already added the marketplace and want the latest plugin metadata:\n\n```bash\ncodex plugin marketplace upgrade\n```\n\n### Plugin structure\n\n```\nplugins/rival-search-mcp/\n├── .claude-plugin/\n│   └── plugin.json       # Claude Code manifest\n├── .codex-plugin/\n│   └── plugin.json       # Codex manifest\n└── .mcp.json             # Registers https://RivalSearchMCP.fastmcp.app/mcp\n\nplugins/rival-search-mcp-skills/\n├── .claude-plugin/\n│   └── plugin.json       # Claude Code skill plugin manifest\n├── .codex-plugin/\n│   └── plugin.json       # Codex skill plugin manifest\n└── skills/\n    └── rival-search-mcp/ # Agent skill, resources, and CLI helper\n```\n\nMarketplace catalogs at:\n- `.claude-plugin/marketplace.json` — Claude Code\n- `.agents/plugins/marketplace.json` — Codex\n\nPlugin package:\n- `plugins/rival-search-mcp/.claude-plugin/plugin.json` — Claude Code manifest\n- `plugins/rival-search-mcp/.codex-plugin/plugin.json` — Codex manifest\n- `plugins/rival-search-mcp/.mcp.json` — hosted MCP server registration\n- `plugins/rival-search-mcp-skills/skills/rival-search-mcp/` — standalone agent skill package\n\n---\n\n## Agent Skills\n\nRivalSearchMCP ships with a **Claude Code Agent Skill** — a self-contained CLI that lets AI agents use all 9 tools without MCP configuration.\n\n### Use as a Claude Code Skill\n\nCopy the skill into your Claude Code skills directory:\n```bash\n# Project-level (available when working in this repo)\ncp -r skills/rival-search-mcp .claude/skills/\n\n# Global (available in all projects)\ncp -r skills/rival-search-mcp ~/.claude/skills/\n```\n\nClaude will automatically discover the skill and use the CLI when you ask for web research, competitor analysis, or content discovery.\n\n### Use the CLI directly\n\nThe CLI is self-contained with inline dependencies — just run with `uv`:\n```bash\nuv run skills/rival-search-mcp/scripts/cli.py call-tool web_search --query \"your query\"\nuv run skills/rival-search-mcp/scripts/cli.py call-tool social_search --query \"AI agents\" --platforms reddit\nuv run skills/rival-search-mcp/scripts/cli.py call-tool news_aggregation --query \"tech news\" --time-range week\nuv run skills/rival-search-mcp/scripts/cli.py list-tools\n```\n\n### Skill structure\n```\nskills/rival-search-mcp/\n├── SKILL.md              # Agent instructions (auto-loaded by Claude Code)\n├── scripts/\n│   └── cli.py            # Standalone CLI with all 9 tools\n└── resources/\n    ├── search.md         # web_search, social_search, news_aggregation, github_search, map_website\n    ├── content.md        # content_operations, document_analysis\n    └── research.md       # research_topic, scientific_research\n```\n\n## ⚡ Key Features\n\n- **Multi-Engine Search**: 5 search engines (DuckDuckGo, Bing, Yahoo, Mojeek, Wikipedia) with TLS-fingerprint-safe fetches via Scrapling\n- **9-Platform Social Research**: Reddit, Hacker News, Stack Overflow, Dev.to, Medium, Product Hunt, Bluesky, Lobste.rs, Lemmy\n- **5-Source News Aggregation**: Google News, Bing News, The Guardian, GDELT, DuckDuckGo News — with time-range filtering\n- **5 Academic Databases + 4 Dataset Hubs**: OpenAlex, CrossRef, arXiv, PubMed, Europe PMC + Kaggle, HuggingFace, Dataverse, Zenodo\n- **Deterministic Output**: no LLM runs inside the server; callers' models do the synthesis\n- **Structured `ToolResult`**: `content_operations score` and `find_conflicts` return both markdown (for humans) and parseable JSON (for agent chaining)\n- **Auto-Quality Scoring**: every multi-result tool attaches per-item quality (0-100) and an aggregate confidence signal\n- **Conflict Detection**: finds numeric/date/polarity disagreements across sources with confidence weights\n- **Document Analysis**: PDF / Word / text / images (images via EasyOCR, auto-downloaded)\n- **Production Hygiene**: per-tool timeouts, sliding-window rate limiting, response-size caps, error masking, FastMCP 3.x middleware stack\n- **Zero Authentication**: every tool works without API keys or setup.\n\n## 💬 FAQ\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cstrong\u003eIs RivalSearchMCP really free?\u003c/strong\u003e\u003c/summary\u003e\n\nYes. RivalSearchMCP is free and open source under the MIT License. The core tools do not require paid APIs or subscriptions. The hosted server applies fair-use rate limiting; you can self-host if you need different limits.\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cstrong\u003eDo I need API keys?\u003c/strong\u003e\u003c/summary\u003e\n\nNo. RivalSearchMCP works completely without any API keys, authentication, or configuration. Just add the URL and use all 9 tools immediately.\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cstrong\u003eWhat MCP clients are supported?\u003c/strong\u003e\u003c/summary\u003e\n\nRivalSearchMCP works with any MCP-compatible client including Claude Desktop, Cursor, VS Code, and Claude Code.\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cstrong\u003eCan I self-host this?\u003c/strong\u003e\u003c/summary\u003e\n\nYes. Clone the repo, run `uv sync --extra dev`, then `fastmcp run` (stdio) or `fastmcp run --transport http --host 0.0.0.0 --port 8000` (HTTP). Full instructions are in the Getting Started section above.\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cstrong\u003eWhy is there no in-server LLM / research agent?\u003c/strong\u003e\u003c/summary\u003e\n\nDeliberately. The server returns deterministic, auditable output so the caller's model can reason over it — a consistent machine can't hallucinate the way a synthesizing one can. If you want an autonomous agent loop, run it in your client.\n\u003c/details\u003e\n\n## 🤝 Contributing\n\nContributions are welcome! Whether it's fixing bugs, adding new research tools, or improving documentation, your help is appreciated.\n\n1. **Fork the Project**\n2. **Create your Feature Branch** (`git checkout -b feature/AmazingFeature`)\n3. **Commit your Changes** (`git commit -m 'Add some AmazingFeature'`)\n4. **Push to the Branch** (`git push origin feature/AmazingFeature`)\n5. **Open a Pull Request**\n\n## 💡 Issues, Feedback \u0026 Support\n\nFound a bug, have a feature request, or want to share how you're using RivalSearchMCP? We'd love to hear from you!\n\n- **Report a bug** — Help us improve by reporting issues\n- **Request a feature** — Suggest new capabilities you'd find useful\n- **Share your use case** — Tell us how you're using RivalSearchMCP\n\n👉 **[Open an Issue](https://github.com/damionrashford/RivalSearchMCP/issues)**\n\n## Attribution \u0026 License\n\nThis is an open source project under the **MIT License**. If you use RivalSearchMCP, please credit it by linking back to [RivalSearchMCP](https://github.com/damionrashford/RivalSearchMCP). See [LICENSE](LICENSE) file for details.\n\n## ⭐ Like this project? Give it a star!\n\nIf you find RivalSearchMCP useful, please consider giving it a star. It helps others discover the project and motivates continued development!\n\n[![Star this repo](https://img.shields.io/github/stars/damionrashford/RivalSearchMCP?style=social)](https://github.com/damionrashford/RivalSearchMCP)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdamionrashford%2Frivalsearchmcp","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdamionrashford%2Frivalsearchmcp","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdamionrashford%2Frivalsearchmcp/lists"}