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Glama

Crawl

crawl
Read-only

Discover and scrape a whole site as one job. Returns a crawl id straight away; read it with crawl_status. Use this instead of calling scrape in a loop.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesThe site to start from.
limitNoMaximum pages to scrape.
maxDepthNoHow far from the seed to follow.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already mark the tool as read-only and non-destructive. The description adds the key behavioral trait that the tool returns a crawl id immediately and that results must be retrieved later via crawl_status. This is useful beyond the structured annotations, though it does not cover failure modes or rate limits.

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 two sentences with no filler. It front-loads the core purpose, then covers the return contract and the alternative usage pattern efficiently.

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?

With strong schema coverage, safety annotations, and no output schema, the description tells the agent what the tool does, what it returns, and how to consume the result. That is sufficient for correct invocation and follow-up.

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 the schema already documents url, limit, and maxDepth. The description adds no parameter-specific meaning beyond framing the job as whole-site crawling, so the baseline of 3 applies.

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 states a specific verb and resource: 'Discover and scrape a whole site as one job.' It also distinguishes itself from the scrape sibling by saying 'Use this instead of calling scrape in a loop,' making the tool's role 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 gives an explicit when-to-use instruction with a named alternative: use this instead of calling scrape in a loop. It also tells the agent where to read results via crawl_status, so the consumption path is clear.

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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TDQS

A3.6/5.0
Disambiguation3/5

Most tools target distinct output types like search results, screenshots, images, or styleguides, but scrape and extract overlap significantly in pulling structured data from URLs. Brand and logo also intentionally overlap, with logo being a cheaper subset, though the descriptions do help clarify when to use each.

Naming Consistency3/5

All tool names are short and lowercase, but they mix imperative verbs like extract, map, scrape, and search with noun-style names like brand, images, logo, and styleguide. There is no consistent verb-noun pattern, though the names remain readable and memorable.

Tool Count4/5

Nine tools is a reasonable size for a web data-fetching server, and each tool covers a plausible retrieval mode. The set is not perfectly lean due to some functional overlap, but nothing feels excessive or redundant enough to hurt usability.

Completeness4/5

The toolset covers discovery via search and map, content extraction via scrape and extract, and visual/asset needs via screenshot, images, brand, logo, and styleguide. Minor gaps like PDF extraction or raw HTML retrieval exist, but agents can usually work around them.

Resources