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ZOOQ - LinkedIn Data for AI Agents

by io.github.baldiga

profile_employment_history

Read-onlyIdempotent

Complete LIVE work history for one person: per-role organization, title, description, location, parsed dates, per-role skills, and parallel-position groupings (concurrent titles kept distinct rather than flattened). Overlaps profile_full_experience, which reads the dataset record — use this when you need freshness, per-role skills, or correct handling of concurrent roles. Pass handle and Zooq resolves it at no extra credit cost, or pass entityId from profile_entity_id to skip the lookup. Not-found is free upstream. (Costs 10 Zooq credits.)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
handleNoPublic profile handle — the part after linkedin.com/in/ — or the full profile URL. Resolved to `entityId` automatically at no extra credit cost. Any person identifier is accepted here and sorted by format (handle, URL, ACoAA… entityId, prsn_ id). Provide `entityId` OR `handle`; `handle` is the simplest.
entityIdNoLive 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 `entityId` OR `handle`.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already cover read-only/idempotent/open-world safety, and the description adds meaningful behavioral detail beyond them: the result is LIVE, concurrent roles are preserved rather than flattened, handle resolution costs no extra credits, not-found profiles are free upstream, and the call costs 10 Zooq credits. No contradiction exists between the description and annotations.

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

Conciseness4/5

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

The description is dense but every sentence carries useful information: result contents, sibling differentiation/usage criteria, parameter guidance, and cost. The first sentence is a long enumeration and the cost detail is buried at the end, preventing a perfect structure score.

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?

Given the output schema exists, return values need no deep explanation, and the description already summarizes the key result fields. It covers freshness, sibling routing, parameter choice, cost, and not-found behavior — nothing needed for correct selection or invocation appears 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%, so the baseline is 3. The schema already documents handle/entityId formats, auto-resolution, accepted aliases, and the OR relationship. The description restates this guidance rather than adding semantically new parameter information beyond the schema.

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 names a specific resource ('LIVE work history for one person') and enumerates the exact content returned: per-role organization, title, description, location, parsed dates, skills, and concurrent-role groupings. It also distinguishes itself from profile_full_experience by contrasting live data freshness against dataset-record reads. This is a precise, differentiated definition.

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 states when to use this tool ('when you need freshness, per-role skills, or correct handling of concurrent roles') and explicitly names the overlapping alternative (profile_full_experience) and why to avoid it there. It also gives parameter usage guidance (handle vs entityId), including that handle resolution has no extra credit cost and entityId skips the lookup.

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.

Resources