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 declare read-only, idempotent, non-destructive behavior, and the description adds the non-obvious cost of 10 Zooq credits plus the specific output fields returned. This is useful context beyond the structured metadata and contains no contradiction.
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 compact two-part sentence: it states the result in the opening clause and appends the cost parenthetically. Every element earns its place, with no filler or repetition.
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 one-parameter, read-only tool with an output schema and rich annotations, the description plus schema covers purpose, input source, returned fields, and cost. Nothing essential for an agent to select and invoke the tool correctly 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 opportunityEntityId fully, including sourced path examples from search_jobs and companies_jobs. The description itself adds no further parameter-level meaning, 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 identifies the tool's output as 'similar job postings' with concrete fields (title, organization, location, salary range, posted date), which clearly distinguishes it from siblings like jobs_details_v2 or jobs_people_also_viewed. It lacks an explicit verb, but the title 'Find similar jobs' and the resource context make the purpose unmistakable.
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 no guidance on when to choose jobs_similar over alternatives such as jobs_details_v2 or jobs_people_also_viewed. It only implies usage through the name and purpose; the source instructions for opportunityEntityId live in the schema, not the description.
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.