Batch status
batch_statusRead a batch started with batch: how far it has got, and the results so far.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| batchId | Yes | The id returned by batch. |
batch_statusRead a batch started with batch: how far it has got, and the results so far.
| Name | Required | Description | Default |
|---|---|---|---|
| batchId | Yes | The id returned by batch. |
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 and destructiveHint=false, so the safety profile is covered. The description adds that the call returns current progress and results-so-far, but gives no detail on output shape, pagination, or polling behavior.
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?
A single front-loaded sentence conveys the operation, the object, and the key output without filler. Every phrase earns its place.
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 one-parameter, read-only status tool with a simple schema, the description covers the essential return value: progress and results so far. It does not enumerate output fields, but the complexity is low and no output schema exists, so this is sufficient.
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 the single batchId parameter already contains the description 'The id returned by batch.' The tool description adds no additional parameter semantics beyond what the schema already provides.
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 names a specific action (read) and a specific resource (a batch started by the batch tool), and clarifies that it reports progress and partial results. This clearly differentiates it from the sibling tool 'batch', which starts batches.
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?
It clearly ties usage to batches created by the 'batch' tool, so an agent knows when to call it. It does not explicitly mention alternatives like 'crawl_status' or state when not to use it, so it stops short of full routing guidance.
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.