Full job-posting details — title, description, functions, apply url, organization, location. (Costs 10 Zooq credits.)
| Name | Required | Description | Default |
|---|---|---|---|
| opportunityEntityId | Yes | Numeric job posting id. Get it from search_jobs — read data.jobs[].id — or companies_jobs — read data.jobs[].jobID. |
| Name | Required | Description | Default |
|---|---|---|---|
| location | No | ||
| jobDetails | No | ||
| organization | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already cover read-only, idempotent, non-destructive behavior, so the description's additional disclosure of the 10-credit cost adds meaningful behavioral context beyond what annotations provide. The description is consistent with the annotations, with no contradictions.
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 two short sentences with zero filler. It front-loads the purpose and return fields, then adds the cost disclosure, making every sentence valuable.
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-only, idempotent tool with a full output schema and complete parameter documentation, the description covers everything needed to invoke it correctly. The cost disclosure is a useful extra, and nothing important is missing.
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?
The input schema already documents the single required parameter with 100% coverage, including where to source the ID and what format it should be. The description adds no extra parameter-level meaning beyond the schema, so the baseline of 3 is appropriate.
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 clearly states that the tool returns full job-posting details and enumerates the included fields: title, description, functions, apply URL, organization, and location. This gives a specific resource and outcome, but it does not explicitly distinguish it from sibling tools like jobs_hiring_team or jobs_similar, so it stops short of a 5.
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 gives clear operational context by telling the agent to obtain the opportunityEntityId from search_jobs or companies_jobs, which effectively explains when to call this tool after listing jobs. However, it does not explicitly state when not to use it or contrast it with alternative job-detail-related sibling tools.
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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Many tools have strongly overlapping purposes: companies_name_lookup is explicitly equivalent to search_companies, companies_enrich/companies_info/companies_universal_name_to_id all return company-profile data, and profile_full overlaps with profile_employment_history and profile_enrich. The descriptions are detailed, but an agent would still frequently have to choose between near-duplicate endpoints.
Tool names mostly follow a predictable resource-prefixed snake_case pattern, such as companies_*, jobs_*, posts_*, profile_*, and search_*, which makes the set readable and groupable. Minor inconsistencies like jobs_details_v2, g_title_skills_lookup, and mixed noun suffixes (info/details/full/lookup) keep it from a perfect score.
44 tools is well beyond the heavy 25+ band, and several tools appear to be different lookup modes or near-duplicates of the same underlying capability. The broad LinkedIn-style data domain explains much of the size, but the set still feels bloated rather than well-scoped.
The API covers the core read-only professional-data workflows well: people, companies, jobs, posts, comments, likes, email discovery/verification, schools, skills, and targeted searches. Minor gaps exist, such as some job filters being unusable and no direct exposure of certain profile alias endpoints, but agents can generally complete end-to-end workflows.