greenhouse_b2b_tech_hiring_velocity_scraper

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

No annotations are provided, so the description carries the full behavioral burden. It never states that this is a read-only scrape, whether results are live or cached, what the result count/freshness limits are, or what a failure looks like. Only the word 'scraper' in the name hints that it is a non-mutating extraction.

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 well-formed sentence with no filler or repetition. It is front-loaded with the domain scope, though the phrase 'API:' adds no information over the tool name.

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?

With no annotations and no output schema, the description should explain what is extracted per item (role counts? company names? posting dates?), how freshness is handled, and what 'velocity' means here. None of that is present, leaving the agent unable to predict the response or judge result quality.

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 parameter (maxResults) with full schema description coverage, so the schema already explains it. The description adds nothing about how many items are typical, what happens at the default of 10, or whether truncation is silent. Baseline 3 applies since the schema does the work.

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 data domain (open engineering and GTM roles at high-growth B2B tech companies on Greenhouse ATS), which is more informative than the bare tool name. However, it is phrased as a dataset/API label rather than an action, so it never says what invoking the tool actually does (extract current listings? compute velocity metrics?). It also does not differentiate itself from the many sibling Greenhouse hiring tools, so an agent cannot pick between them from this text.

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?

There is no when-to-use guidance, no when-not-to-use exclusion, and no mention of adjacent alternatives such as the AI/ML engineering radar or the fintech compliance hiring tracker. The reader is left to infer that this is the generic B2B tech version by name alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.