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MetaliftAI

metalift-mcp

Official
by MetaliftAI

Batch Scrape URLs

metalift_batch_scrape
Read-only

Batch scrape up to 100 URLs in parallel into LLM-ready markdown, with background job support and options for JavaScript-heavy pages.

Instructions

Scrape up to 100 URLs in parallel (billed per URL). Default response_detail=compact. Use async=true for background jobs; wait=true blocks with progress. For JS-heavy pages set strategy=auto in scrape_options. You are solely responsible for complying with website terms, robots.txt, copyright, and data protection laws when using scraped content.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlsYesURLs to scrape in parallel
waitNoWhen async=true, wait for completion with progress updates (default true).
asyncNoRun as background job (returns job id)
scrape_optionsNoShared scrape options applied to every URL in the batch
Behavior4/5

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

Annotations already declare readOnlyHint and openWorldHint, so the description adds complementary context: billing per URL, default response_detail, async/wait behavior, and a strategy recommendation. It also includes legal responsibility, which is extra transparency. It does not repeat annotation info and provides value beyond the structured fields.

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 three sentences, front-loads the core purpose and billing, then covers options and legal note. Every sentence earns its place; no filler or repetition of schema fields. It is appropriately sized for the tool's complexity.

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

Completeness4/5

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

For a tool with 4 parameters, nested objects, and no output schema, the description covers the main usage modes (sync/async), defaults, and legal notes. It does not explicitly describe the return format, but the async/wait semantics and job id mention partially compensate. Given the absence of an output schema, this is reasonably complete, though an explicit return description would push it to 5.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3. The description adds meaning by noting the default `response_detail=compact` (not in schema), clarifying `async=true` yields a job id, and recommending `strategy=auto` for JS-heavy pages—these details go beyond the schema's basic descriptions.

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 uses a specific verb+resource+scope: 'Scrape up to 100 URLs in parallel.' It clearly distinguishes from the sibling `metalift_scrape` by emphasizing batch capability, and references paid billing and defaults, making the tool's purpose unambiguous.

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

Usage Guidelines4/5

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

Provides clear context on when to use `async=true` (background jobs) and `wait=true` (block with progress), and recommends `strategy=auto` for JS-heavy pages. While it doesn't explicitly name alternatives like `metalift_scrape` for single URLs, the 'up to 100 URLs' phrasing implies the batch use case. The guidance is actionable and helps select appropriate parameters.

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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