Multi-source web search. Aggregates results from Brave Search and/or Tavily (whichever has a key configured) with dedup and cross-source scoring. Returns title, URL, snippet, and source for each result. Use as the default 'search the web' tool.

Fetch any URL and return clean markdown content. Uses Jina Reader which handles JS rendering and bot detection on your behalf, returning readable text. Ideal for reading articles, papers, docs, or blog posts.

Search Wikipedia. Returns titles, URLs, and snippets. No API key required.

Search arXiv for academic preprints across all fields (CS, physics, math, bio, etc.). Returns title, authors, abstract snippet, and PDF URL. No API key required.

Search Hacker News for tech news, discussions, and trending links. Returns title, URL, points, comments, and dates. No API key required.

Search Stack Exchange sites (default: Stack Overflow) for high-quality technical Q&A. Set the 'site' parameter to search other SE communities (e.g. 'serverfault', 'superuser', 'askubuntu', 'math', 'tex'). No API key required.

Search Crossref for scholarly metadata across publishers (Elsevier, Springer, Wiley, IEEE, ACM, etc.). Returns DOI, citation count, publication date, and abstract. Covers peer-reviewed papers that arXiv may not. No API key required.

Build a structured research plan for a complex question without executing it. Returns the planned sub-questions, the sources recommended for each, and estimated cost (searches + fetches). Use this when you want to review or edit the plan before committing to the full deep-research pipeline. Returns JSON; pass it back to the research tool to execute.

Fetch a URL and extract the passages most relevant to a specific question. Returns each passage with a short context window before/after, a relevance score, and a character offset into the original page so citations are verifiable. Use this when you want to drill into a specific source for evidence on a narrow claim.

Run a full deep-research pipeline on a complex question. Decomposes the question into sub-questions, fans out across multiple sources (Wikipedia, arXiv, Hacker News, Stack Exchange, Crossref, plus Brave/Tavily if keys are configured), fetches the top URLs, extracts the most relevant passages, and returns a structured ResearchReport containing: the plan, a numbered citation manifest, per-sub-question evidence with quotes + character offsets, and a Markdown synthesis template for you to fill in. You (the model) should write the narrative synthesis citing the [n] markers; the server does the gathering, not the writing. Use depth='quick' for fast overviews, 'standard' for normal research, 'deep' for thorough multi-source investigations.