Web Research Assistant

Look up package information from npm, PyPI, crates.io, or Go modules. Returns version, downloads, license, dependencies, security status, and repository links. Use this to quickly evaluate libraries before adding them to your project. Examples: - package_info("express", reasoning="Need web framework", registry="npm") - package_info("requests", reasoning="HTTP client for API", registry="pypi") - package_info("serde", reasoning="JSON serialization", registry="crates")

Search for code examples, tutorials, and technical articles. Optimized for finding practical examples and learning resources. Can optionally filter by time range for the most recent content. Perfect for learning new APIs, finding usage patterns, or discovering how others solve specific technical problems. Content Types: - 'code': GitHub repos, code snippets, gists, Stack Overflow code examples - 'articles': Blog posts, tutorials, documentation, technical articles - 'both': Mix of code and written content (default) Time Ranges: - 'all': Search all available content (default, recommended for best results) - 'year', 'month', 'week', 'day': Filter to recent content only Examples: - search_examples("FastAPI dependency injection examples", content_type="code") - search_examples("React hooks tutorial", content_type="articles", time_range="year") - search_examples("Rust lifetime examples", content_type="both")

Search for high-quality stock images using Pixabay. Returns royalty-free images that are safe to use. Perfect for finding photos, illustrations, and vector graphics for projects, presentations, or design work. Image Types: - 'photo': Real photographs - 'illustration': Digital illustrations and artwork - 'vector': Vector graphics (SVG format available) - 'all': All types (default) Examples: - search_images("mountain landscape", image_type="photo") - search_images("business icons", image_type="vector") - search_images("technology background", orientation="horizontal")

Search for packages by keywords or description across registries. Use this to find packages that solve a specific problem or provide certain functionality. Perfect for discovering libraries when you know what you need but not the package name. Examples: - package_search("web framework", reasoning="Need backend framework", registry="npm") - package_search("json parsing", reasoning="Data processing", registry="pypi")

Fetch GitHub repository information and health metrics. Returns stars, forks, issues, recent activity, language, license, and description. Use this to evaluate open source projects before using them. Examples: - github_repo("microsoft/vscode", reasoning="Evaluate editor project") - github_repo("https://github.com/facebook/react", reasoning="Research UI framework")

Find solutions for error messages and stack traces from Stack Overflow and GitHub. Takes an error message or stack trace and finds relevant solutions with code examples. Automatically detects language and framework, extracts key error information, and searches for the best solutions ranked by votes and relevance. Perfect for: - Debugging production errors - Understanding cryptic error messages - Finding working code fixes - Learning from similar issues Examples: - translate_error("TypeError: Cannot read property 'map' of undefined", reasoning="Debugging React app crash") - translate_error("CORS policy: No 'Access-Control-Allow-Origin' header", reasoning="Fixing API integration", framework="FastAPI") - translate_error("error[E0382]: borrow of moved value", reasoning="Learning Rust ownership", language="rust")

Search and fetch official API documentation with examples and explanations. Documentation-first approach: fetches human-written docs with context, examples, and best practices. Much more useful than OpenAPI specs alone. Discovery strategy: 1. Try common URL patterns (docs.{api}.com, {api}.com/docs, etc.) 2. If patterns fail, search for "{api} API official documentation" 3. Crawl discovered docs and extract relevant content No hardcoded URLs - works for ANY API by discovering docs dynamically. Examples: - api_docs("stripe", "create customer", reasoning="Setting up payments") - api_docs("github", "create repository", reasoning="Automating repo creation") - api_docs("spartan", "button component", reasoning="Learning UI library")

Extract structured data from web pages. Extracts tables, lists, or specific fields from HTML pages and returns structured data. Much more efficient than parsing full page text. Extract Types: - "table": Extract HTML tables as list of dicts - "list": Extract lists (ul/ol/dl) as structured list - "fields": Extract specific elements using CSS selectors - "json-ld": Extract JSON-LD structured data - "auto": Automatically detect and extract structured content Examples: - extract_data("https://pypi.org/project/fastapi/", reasoning="Get package info") - extract_data("https://github.com/user/repo/releases", reasoning="Get releases", extract_type="list") - extract_data( "https://example.com/product", reasoning="Extract product details", extract_type="fields", selectors={"price": ".price", "title": "h1.product-name"} )

Compare multiple technologies, frameworks, or libraries side-by-side. Automatically gathers information about each technology and presents a structured comparison to help make informed decisions. Categories: - "framework": Web frameworks (React, Vue, Angular, etc.) - "library": JavaScript/Python/etc. libraries - "database": Databases (PostgreSQL, MongoDB, etc.) - "language": Programming languages (Python, Go, Rust, etc.) - "tool": Build tools, CLIs, etc. (Webpack, Vite, etc.) - "auto": Auto-detect category Examples: - compare_tech(["React", "Vue", "Svelte"], reasoning="Choose framework for new project") - compare_tech(["PostgreSQL", "MongoDB"], category="database", reasoning="Database for user data") - compare_tech(["FastAPI", "Flask"], aspects=["performance", "learning_curve"], reasoning="Python web framework")