Batch
batchScrape a list of URLs you already have as one job. Returns a batch id straight away; read it with batch_status.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| urls | Yes | The URLs to scrape. |
batchScrape a list of URLs you already have as one job. Returns a batch id straight away; read it with batch_status.
| Name | Required | Description | Default |
|---|---|---|---|
| urls | Yes | The URLs to scrape. |
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?
The description discloses the key asynchronous behavior beyond what annotations provide: it returns a batch id immediately and requires later polling via batch_status. The readOnlyHint and openWorldHint annotations are not contradicted by any wording.
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 two short sentences with no filler. The main action is front-loaded, and the follow-up polling mechanism is placed directly after, giving a clean and efficient structure.
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?
Given a single fully documented parameter, no output schema, and annotations that already cover the safety profile, the description is complete. It covers the essential flow: submit a list, get a batch id, then read via batch_status.
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?
The input schema already fully describes the only parameter ('The URLs to scrape'), so the description adds limited semantic value. The phrase 'you already have' slightly clarifies that URLs are pre-existing rather than discovered, but this is a minor addition.
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 action ('Scrape') on a specific resource ('a list of URLs') and adds the batch scope ('as one job'). It implies a distinction from crawl-style discovery with 'you already have,' but it does not explicitly name alternatives or differentiate from the single-URL 'scrape' sibling.
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 clearly signals when to use this tool: when you already have a list of URLs to scrape. It also directs the user to batch_status for retrieving results, which is a concrete usage pointer. It does not explicitly state exclusions or alternative tools, but the context is clear enough.
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.