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ZOOQ - LinkedIn Data for AI Agents

by io.github.baldiga

jobs_posted_by_profile

Read-onlyIdempotent

Job postings authored by a person (a recruiter's, hiring manager's or founder's roles). Includes closed postings (jobState). Only people who have posted jobs return results: for anyone else the upstream answers 422 "the data cannot be displayed or it doesn't exist" - that is a not-found, not a bad id. Find posters via jobs_hiring_team on a live posting. (Costs 10 Zooq credits.)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
countNoResults per page, 1-25 (default 10).
startNoPagination offset.
handleNoPublic profile handle — the part after linkedin.com/in/ — or the full profile URL. Resolved to `personEntityId` automatically at no extra credit cost. Any person identifier is accepted here and sorted by format (handle, URL, ACoAA… entityId, prsn_ id). Provide `personEntityId` OR `handle`; `handle` is the simplest.
personEntityIdNoLive person entityId (ACoAA…) from profile_entity_id / profile_enrich; the urn:li:fsd_profile: form is accepted. A prsn_ id (dataset namespace, from profile_full) or a handle placed here is recognized and translated automatically. Provide `personEntityId` OR `handle`.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
jobsNoArray in the example
totalNoExample value was a number

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already indicate readOnly, idempotent, and non-destructive behavior. The description goes beyond annotations by disclosing the 422 error semantics, the inclusion of closed postings, and the 10-credit cost — all behavior an agent cannot infer from annotations alone.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and dense: main purpose first, then scoping details, error interpretation, alternative workflow, and cost. Every sentence adds non-obvious information without repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a list-style read tool with an output schema and clear annotations, the description covers the essential call decisions: what it returns, when it returns nothing, how to interpret errors, how to find appropriate inputs, and the cost. Nothing critical is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% and the parameter descriptions are rich (handle vs personEntityId, accepted formats, auto-resolution, either/or requirement). The tool description itself adds little parameter-specific meaning beyond context, so the baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the resource and action: "Job postings authored by a person" — a specific, distinguishable operation from sibling tools like jobs_hiring_team and search_jobs. It also adds meaningful scope details, such as including closed postings via `jobState`, which removes ambiguity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly tells when results will be empty/not-found: "Only people who have posted jobs return results" and explains the upstream 422 is a not-found, not a bad ID. It also names the alternative workflow: "Find posters via jobs_hiring_team on a live posting," plus the credit cost, which helps decide whether to call this tool.

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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TDQS

A3.7/5.0
Disambiguation3/5

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.

Naming Consistency4/5

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.

Tool Count2/5

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.

Completeness4/5

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.

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