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web_batch_status

Idempotent

Checks a batch of scrapes: status, how many are done, and the results. Free for the owning account. Pass cancel: true to stop it.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
jobIdYesThe batch, from web_scrape_batch.
limitNoResults to return (default 50).
cancelNoStop pending pages and refund pages that never started. An in-flight read may finish and is billed; failed reads are refunded.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / cancel / description
      Previous value: -"Stop the batch and refund what it did not read."New value: +"Stop pending pages and refund pages that never started. An in-flight read may finish and is billed; failed reads are refunded."
  2. Changed1 schema field changed
    • addedInput schema / properties / jobId / description
      Added value: +"The batch, from web_scrape_batch."
  3. First observed

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already declare the mutation profile (readOnlyHint=false, idempotentHint=true, destructiveHint=false), so the bar is lower. The description nonetheless adds two useful behavioral facts beyond the schema: the call is 'Free for the owning account' (cost context) and passing cancel:true stops the batch, which explains why a status tool is not read-only.

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?

Three short, front-loaded sentences with no padding: capability, cost, and the optional stop flag. Every sentence earns its place and the most important information comes first.

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?

With no output schema, the description correctly takes on the burden of describing the return values ('status, how many are done, and the results'), which is enough for an agent to know what to expect. It stops short of mentioning pagination or how limits interact with large result sets, a minor gap.

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

Parameters3/5

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

Schema description coverage is 100%, so jobId, limit, and cancel are already fully documented in the schema, including the refund/billing nuance for cancel. The description's 'Pass cancel: true to stop it' only restates what the schema says, so the baseline 3 applies.

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

Purpose4/5

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

The description states a specific verb and resource ('Checks a batch of scrapes') and even enumerates what comes back: status, count of completed pages, and results. It implicitly separates itself from the crawl-status sibling by scoping to scrapes, but it never names web_scrape_batch or web_crawl_status directly, so an agent must infer the boundary.

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

Usage Guidelines3/5

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

Usage is only implied: the tool is clearly meant to be called after web_scrape_batch produced a jobId, and the cancel note hints at the stop-then-poll flow. There is no explicit when-to-use/when-not statement and no alternative tool is named for the polling case.

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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