Similar job postings (title, organization, location, salary range, posted date). (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 |
|---|---|---|---|
| total | No | Example value was a number | |
| opportunities | No | Array in the example |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already convey read-only and safe behavior. The description adds the 10 Zooq credit cost, which is a material behavioral constraint not present in annotations, and it lists the return fields. It does not contradict annotations.
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 extremely concise, with the core purpose front-loaded and the cost note as a critical additional detail. Every word earns its place and there is no fluff.
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 tool with complete schema coverage and an output schema, the description is largely sufficient. It states what the tool returns and the cost. The main gap is that it does not indicate any pagination, limit, or sorting behavior, but those may be covered by the output schema. The lack of usage guidance is the primary reason it is not a 5.
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%, and the schema fully describes opportunityEntityId, including how to obtain it from search_jobs or companies_jobs. The description adds no parameter-specific meaning beyond the schema, so 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?
The description clearly identifies the tool as returning similar job postings and lists the key output fields (title, organization, location, salary range, posted date). It distinguishes from obvious sibling tools like jobs_details_v2 by focusing on similarity, but it does not explicitly differentiate from jobs_people_also_viewed, which is a closely related alternative.
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 explicit guidance on when to use this tool versus alternatives. The description implies a user wants similar jobs given some job, but it does not mention jobs_people_also_viewed or other similar-job tools, nor does it state exclusions such as 'use for job details instead'. The agent must infer usage from the name and schema.
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.