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 description discloses a meaningful behavioral detail beyond the annotations: the 10 Zooq credits cost. Annotations already cover read-only, idempotent, open-world, and non-destructive behavior, so the description adds useful context without contradicting structured metadata.
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 a single, front-loaded sentence that lists the key contents and includes the important cost warning. There is no redundant or filler text.
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 an output schema and comprehensive annotations, the description plus schema covers everything needed to call it correctly. The cost disclosure and ID sourcing guidance complete the picture.
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 schema covers the single parameter 100%, including a precise description of how to source it from related tools. The tool description itself adds no parameter-specific detail, so with high schema coverage 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 identifies the resource ('full job-posting details') and enumerates the returned fields, making the tool's purpose obvious. It is distinguishable from sibling tools like jobs_hiring_team or jobs_similar, though it lacks an explicit verb such as 'get'.
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 implies use when complete job details are needed, and the input schema explicitly explains how to obtain the required ID from search_jobs or companies_jobs. However, it does not state when to choose this tool over alternatives or mention any exclusion conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools are separated by domain prefixes and the descriptions are unusually explicit about differences, but there are direct overlaps: companies_name_lookup is the same upstream as search_companies, companies_entity_id vs companies_universal_name_to_id resolve different id spaces, and search_people/search_people_live plus search_companies/search_companies_live cover similar ground. An agent can usually pick correctly, but only after close reading.
The set is consistently snake_case with readable domain prefixes like companies_, jobs_, posts_, profile_, and search_. Deviations include the unexplained g_* prefix, jobs_details_v2's version suffix, affiliate_program lacking a resource prefix, and the duplicate naming convention of companies_name_lookup vs search_companies.
45 tools is well above the 25+ threshold and creates a heavy surface for an agent to scan. While the domains are broad, some tools are redundant (companies_name_lookup/search_companies) or tangential (affiliate_program), so the count is not fully justified.
The server covers people, companies, jobs, posts, email, schools, and skills with both search and detail endpoints, which is strong for a read-only LinkedIn API. Obvious gaps like a global post search or a company followers list are absent, but the existing paths support most workflows without dead ends.