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novada_scrape

Read-only

Extract structured tabular records from 129 platforms like Amazon, Reddit, and LinkedIn using platform-specific scraping operations. Get product data, social posts, job listings, and reviews.

Instructions

Use when you need structured data from a specific platform — not raw HTML, but clean tabular records. Supports 129 platforms: Amazon, Reddit, TikTok, LinkedIn, Google Shopping, Glassdoor, GitHub, Zillow, Airbnb, and more.

Best for: E-commerce product data, social posts/comments, job listings, reviews, real estate, market data. Not for: General web pages (use novada_extract), unknown domains not in the platform list (use novada_crawl). Output formats: "markdown" (default, agent-optimized table), "json" (structured, for programmatic use). Example: platform="amazon.com", operation="amazon_product_keywords", params={keyword:"iphone 16", num:5} Discover platforms: Read the novada://scraper-platforms MCP resource for the complete platform list with operation IDs and required params.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
platformYesPlatform domain to scrape. E.g. 'amazon.com', 'reddit.com', 'tiktok.com', 'linkedin.com', 'google.com'.
operationYesScraping operation ID. Examples: 'amazon_product_keywords', 'amazon_product_asin', 'tiktok_posts_url', 'linkedin_company_information_url', 'github_repository_repo-url', 'twitter_profile_username', 'youtube_video_search_label'. Read novada://scraper-platforms resource for the complete list with required params.
paramsYesOperation-specific parameters. E.g. { keyword: 'iphone 16', num: 5 } for keyword search, { url: 'https://...' } for URL-based ops, { asin: 'B09...' } for ASIN lookup.
limitYesMax records to return. Default 20, max 100.
formatYesOutput format. 'markdown' (default): structured table, easy to read and reason over. 'json': raw records array for programmatic processing. 'toon': token-optimized pipe-separated format (40-65% smaller than JSON/markdown).markdown
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate readOnly=true and destructive=false. The description adds context on output formats (markdown, json, toon), example usage, and references a resource for operation details. It doesn't contradict annotations and provides useful behavioral insight.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and well-structured with clear sections, bullet points, and a code block example. Every sentence serves a purpose without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite having no output schema, the description covers all necessary aspects: purpose, usage guidance, parameter details with examples, output format options, and cross-referencing a resource for more info. It is fully complete for the tool's complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

All 5 parameters have schema descriptions (100% coverage). The description adds significant value by providing concrete examples for platform, operation, and params, explaining the limit default/max, and describing each format. This greatly aids the agent.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states the tool extracts structured data from specific platforms, lists supported platforms, and distinguishes from sibling tools like novada_extract and novada_crawl, making the purpose crystal clear.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It provides clear when-to-use (best for e-commerce, social, etc.) and when-not-to-use (general web pages, unknown domains) guidance, with specific sibling tool names, and directs to a resource for discovering platforms and operations.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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