Item count and per-field coverage of a Scrapy Cloud job without downloading items: how many items were scraped and, for each field, how many items have it (count and percentage). A field at 0% coverage usually means a broken extractor. Use it to validate a finished job's results before reading samples.
| Name | Required | Description | Default |
|---|---|---|---|
| job | Yes | Job key in project/spider/job form, for example 123/1/4. |
Changes observed during successful MCP inspections.
No schema history has been recorded yet.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral load, and it does disclose that no items are downloaded (a cheap metadata read) plus an interpretation rule that 0% coverage signals a broken extractor. It does not mention auth/permission requirements or what happens if the job has not finished, which are the remaining behavioral gaps.
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?
Three short sentences, front-loaded with what is returned, then the diagnostic caveat, then the intended use. Nothing is padding; each sentence adds either output semantics, interpretation, or routing.
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?
There is no output schema, so the description must explain the return shape, and it does: total item count plus per-field count and percentage. For a one-parameter read-only stats tool this is sufficient to call it correctly.
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?
There is a single 'job' parameter with 100% schema description coverage, including a pattern and an example, so the schema does the work. The description only refers to 'a Scrapy Cloud job' generically and adds no format details beyond what is already documented.
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 the exact resource (a Scrapy Cloud job) and the exact computation (item count plus per-field coverage), and the phrase 'without downloading items' functionally separates it from the sibling scrapy_cloud_get_job_items. An agent can tell what it produces without opening any schema.
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 gives an explicit use case — 'validate a finished job's results before reading samples' — which implies the ordering against get_job_items, but it never names the alternative tool or states when not to use this one (e.g., for a still-running job).
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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