ZOOQ - LinkedIn Data for AI Agents

profile_enrich

Read-onlyIdempotent

Freshest LIVE snapshot of one profile, by handle or entityId — not the deduplicated dataset record the other profile/* endpoints return. Carries live-only flags (openToWork, isHiring, isTopVoice) and returns the person's entityId, the id every other live person endpoint needs. (Costs 10 Zooq credits.)

Input Schema

TableJSON Schema

Name	Required	Description	Default
`handle`	No	Public profile handle. Provide handle OR entityId (entityId wins if both).
`entityId`	No	Person entityId from a previous profile_enrich or profile_entity_id call. Provide handle OR entityId.

Output Schema

TableJSON Schema

Name	Required	Description
`handle`	No	Example value was a string
`premium`	No	Example value was a boolean
`entityId`	No	Example value was a string
`fullName`	No	Example value was a string
`headline`	No	Example value was a string
`industry`	No	Example value was a string
`isHiring`	No	Example value was a boolean
`lastName`	No	Example value was a string
`location`	No
`firstName`	No	Example value was a string
`influencer`	No	Example value was a boolean
`isTopVoice`	No	Example value was a boolean
`openToWork`	No	Example value was a boolean
`followerCount`	No	Example value was a number
`connectionsCount`	No	Example 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 cover read-only, idempotent, open-world, and non-destructive behavior. The description adds meaningful context beyond those: it returns live-only flags, emphasizes this is a LIVE snapshot rather than the deduplicated record, and discloses a concrete cost of 10 Zooq credits. No contradiction with annotations.

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?

Three sentences, each earning its place: purpose and distinction, return payload highlights, and cost. The most decision-relevant information is front-loaded in the first sentence.

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 two-parameter, read-only tool with a rich output schema, the description covers purpose, scope, exclusions, downstream usage, key return value, and cost. It is sufficiently complete for an agent to select and invoke the tool correctly.

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 schema already explains that handle OR entityId can be provided and that entityId wins when both are present. The description only reiterates 'by handle or entityId' without adding parameter-level detail, so the baseline of 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?

States a specific verb ('snapshot'), resource ('one profile'), and input mechanism ('by handle or entityId'), and immediately distinguishes itself from the deduplicated records returned by other profile/* endpoints. Also lists live-only flags and the key output entityId, which differentiates it from siblings like profile_full or profile_entity_id.

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?

Explicitly says this is NOT the deduplicated dataset record other profile/* endpoints return, giving a clear when-not signal. It also explains that the returned entityId is required by every other live person endpoint, implying this tool is the right entry point when you need live data or a downstream entityId.

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.

profile_enrich

Input Schema

Output Schema

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TDQS

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