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RapierCraft

alterlab-mcp-server

by RapierCraft

alterlab_batch_scrape

Scrape multiple URLs simultaneously and get all results back in one synchronous call. Handles partial failures and supports JavaScript rendering.

Instructions

Scrape multiple URLs simultaneously and get all results back in one call — no polling required. Unlike alterlab_batch (async with batch_id polling), this returns all results inline when complete. Best for 2-20 URLs where you need results immediately. For 20+ URLs, use alterlab_batch instead. Uses concurrent scraping (controlled by max_concurrent) for speed. Shows per-URL status, content preview, and a total cost summary at the end. Handles partial failures gracefully — failed URLs show error details, successful ones show content.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlsYesList of URLs to scrape (max 20). All results are returned synchronously.
formatsNoOutput formats applied to every URL. 'markdown' is best for LLM consumption.
timeoutNoRequest timeout in seconds per URL (1-300).
render_jsNoRender JavaScript using headless browser for all URLs. Required for JS-heavy sites. Increases cost to tier 4 (~$0.001/req).
use_proxyNoRoute all requests through premium proxy (+~$0.0002/req).
max_concurrentNoMaximum number of URLs to scrape in parallel (1-10, default 3). Higher values finish faster but consume more concurrent connections.
Behavior5/5

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

With no annotations, the description fully explains behavior: concurrent scraping, per-URL status, content preview, cost summary, and graceful handling of partial failures.

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?

Concise yet comprehensive; front-loaded with core purpose, then contrasts with sibling, usage guidelines, and behavioral insights—every sentence is informative.

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?

For a 6-parameter tool with no output schema or annotations, the description covers purpose, usage, behavior, and results sufficiently, making it easy for an agent to use correctly.

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 provides full parameter descriptions (100% coverage), but the description adds value by explaining the use case and concurrency behavior, integrating parameter choices into overall context.

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 clearly states the tool scrapes multiple URLs simultaneously and returns results synchronously, distinguishing it from alterlab_batch which uses async polling.

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?

Explicitly recommends using this tool for 2-20 URLs when immediate results are needed, and directs users to alterlab_batch for 20+ URLs, providing clear when-to-use and when-not-to-use guidance.

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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