linkfetch_get_job_applicants

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

With no annotations, the description carries the full burden and does usefully disclose per-call credit cost (1 credit) and the observation window ('since LinkFetch started watching it'). It does not cover behavior when a posting was never observed, error cases, or auth/rate-limit constraints, so the disclosure is good but incomplete.

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 what-it-returns and why-to-use-it content is tightly front-loaded in the first two clauses. The closing sentence is longer and doubles as a sales pitch for the paid dataset, though it does carry actionable routing 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 single-parameter read tool with no output schema, the description adequately conveys what comes back (timestamped count history plus velocity) and the cost of the call. It stops short of describing the response shape or the behavior for postings with no observed history.

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 coverage is 100% and the single `id` parameter is fully documented with a regex pattern and an example LinkedIn URL. The description adds no format or validation detail beyond 'one job posting', so the schema does all the work — the baseline 3 applies.

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?

States a specific verb ('Fetch') and a precisely scoped resource: the observed applicant-count time series for one posting, including every timestamped change plus derived velocity. This is clearly distinguishable from sibling posting tools like linkfetch_get_job, which return the posting itself rather than its applicant history.

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?

Gives explicit analytic intent ('judge how contested a role is, whether interest has stalled, how fast the funnel is filling') and names the bulk alternative (the jobs dataset) with its own pricing. It does not, however, contrast itself against the closest sibling, linkfetch_get_job, so the agent must infer the boundary.

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