Crawl status
crawl_statusRead a crawl started with crawl: how far it has got, and the pages it has scraped.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| crawlId | Yes | The id returned by crawl. |
crawl_statusRead a crawl started with crawl: how far it has got, and the pages it has scraped.
| Name | Required | Description | Default |
|---|---|---|---|
| crawlId | Yes | The id returned by crawl. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, openWorldHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral detail about what the call returns: progress and scraped pages. No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no filler. It efficiently communicates the action, the target resource, and the key output information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple single-parameter, read-only status tool, the description is sufficient: it names the input, the purpose, and the high-level output. It could mention specific status fields or edge cases, but this is not essential for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, and crawlId is already documented as 'The id returned by crawl.' The description does not need to add parameter-level meaning, so a baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Read') and resource ('a crawl'), and clearly ties it to the crawl operation by saying 'started with crawl'. This distinguishes it from siblings like crawl, scrape, and batch_status.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is used after starting a crawl, which gives clear context for when to call it. It does not explicitly exclude alternatives like batch_status, but the reference to 'a crawl started with crawl' is enough to route an agent correctly.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.