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

Beyond the readOnlyHint annotation, the description discloses meaningful operational behavior: the API is Beta and may return HTTP 403 without plan access, each returned posting costs 1 credit, and the endpoint is POST /job_posting/search. This adds cost and auth context that annotations do not provide.

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 compact and front-loaded with the tool's core purpose and dataset, followed by caveats and endpoint. Every sentence carries useful information, and there is no filler or repetition of schema details.

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?

The description covers the key operational factors: dataset scope, query modes, beta/auth caveat, credit cost, and endpoint. With no output schema, it does not describe the response shape or pagination flow, but the scroll_token parameter in the schema partially covers pagination.

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%, so the schema already explains all five parameters. The description adds a high-level note about the two query modes but does not add substantive semantics for individual parameters 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 uses a specific verb ('Search') and a distinct resource ('PDL's Job Posting Dataset'), and clarifies the dataset is sourced from company career pages. This clearly separates it from the sibling tools that search people, companies, or IP data.

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 establishes the dataset domain and the two supported query modes (Elasticsearch query object OR SQL string), which gives an agent enough context to select this tool. However, it does not explicitly state when to prefer SQL over Elasticsearch or mention exclusions relative to sibling search tools.

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