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rival-search-mcp

Pesquisa e Web Autor: damionrashford

Deterministic deep research via RivalSearchMCP. 9 tools: 5-engine web search (DuckDuckGo/Bing/Yahoo/Mojeek/Wikipedia), 9-platform social search (Reddit/HN/StackOverflow/Dev.to/Medium/ProductHunt/Bluesky/Lobste.rs/Lemmy), 5-source news (Google/Bing/Guardian/GDELT/DDG), 5 academic DBs (OpenAlex/CrossRef/arXiv/PubMed/EuropePMC), GitHub search, website mapping, content extraction with OCR, and researc

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Atualizado há 4 meses
Ver no GitHub ↗ Licença: MIT

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Ler descrição completa

RivalSearchMCP

You have access to 9 research tools via the CLI at scripts/cli.py. Run all commands with uv run scripts/cli.py.

Every tool returns deterministic, auditable output. There is no in-server LLM — you're the one doing the synthesis.

How to invoke tools

uv run scripts/cli.py call-tool <tool_name> --flag value

Available tools

  • web_search — concurrent search across DuckDuckGo, Bing, Yahoo, Mojeek, Wikipedia. Use for general web queries.
  • social_search — Reddit, Hacker News, Stack Overflow, Dev.to, Medium, Product Hunt, Bluesky, Lobste.rs, Lemmy. Use for community discussions.
  • news_aggregation — Google News, Bing News, The Guardian, GDELT, DuckDuckGo News. Use for current events. Accepts --time-range day|week|month|anytime.
  • github_search — search public GitHub repos. Use for code, libraries, projects.
  • map_website — crawl a site in research / docs / map mode. Use to explore site structure or documentation.
  • content_operations — one tool, six ops (retrieve, stream, analyze, extract, score, find_conflicts). Use to get full page content, rate source quality, or surface disagreements between sources.
  • document_analysis — extract text from PDFs, Word docs, images (image OCR via EasyOCR). Use for document processing.
  • research_topic — end-to-end research workflow for a topic, combining search, content retrieval, and analysis.
  • scientific_research — OpenAlex, CrossRef, arXiv, PubMed, Europe PMC (papers) + Kaggle, HuggingFace, Dataverse, Zenodo (datasets).

When to chain tools

  • Found a URL from search? → content_operations --operation retrieve --url <url>
  • Want to assess source trust before using results? → content_operations --operation score --urls '[…]'
  • Two sources seem to disagree? → content_operations --operation find_conflicts --urls '[…]'
  • Found a PDF link? → document_analysis --url <url>
  • Need to explore a website? → map_website --url <url> --mode docs
  • Need a unified entity profile in one shot? → research_topic --mode entity --topic "OpenAI"

Tool reference

For full flags, types, and defaults for each tool, read:

  • [resources/search.md](resources/search.md) — web_search, social_search, news_aggregation, github_search, map_website
  • [resources/content.md](resources/content.md) — content_operations, document_analysis
  • [resources/research.md](resources/research.md) — research_topic, scientific_research

Output

All tools return structured text to stdout. Errors go to stderr. Exit codes: 0 success, 1 tool error, 2 connection failed.

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