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ZOOQ - LinkedIn Data for AI Agents

by io.github.baldiga

profile_full

Read-onlyIdempotent

Complete profile in one call — positions, education, skills, certifications, geo, follower/connection counts and flags. This is the canonical profile read; the other profile/* paths (overview, details, about, education, skills, certifications, full-experience, social-matrix) are named aliases that return this exact same record. (Costs 10 Zooq credits.)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idNoStable profile id (prsn_...). Get it from profile_full or /search/people — read data.id. Provide handle OR id.
handleNoPublic profile handle. Provide handle OR id.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idNoExample value was a string
geoNo
urlNoExample value was a string
handleNoExample value was a string
summaryNoExample value was a string
headlineNoExample value was a string
educationNoArray in the example
last_nameNoExample value was a string
first_nameNoExample value was a string
is_creatorNoExample value was a boolean
is_premiumNoExample value was a boolean
is_influencerNoExample value was a boolean
follower_countNoExample value was a number
full_positionsNoArray in the example
connections_countNoExample value was a number

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, covering safety and idempotency. The description adds meaningful beyond-annotation context: the 10 Zooq credit cost, the alias-equivalence behavior, and the comprehensive scope of the returned record.

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 dense sentences with no filler: purpose and contents are front-loaded, alias relationships are stated compactly, and the credit cost is isolated at the end. Every sentence earns its place.

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?

With a present output schema, fully documented parameters, safety annotations, and the credit-cost warning, the description covers everything an agent needs to invoke this read-only canonical profile tool correctly. No critical operational detail 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 both parameters are well-documented with the 'Provide handle OR id' constraint baked into the schema. The prose description does not add parameter-level semantics beyond that, so the baseline of 3 for high-coverage schemas 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?

Description opens with a specific verb and resource: 'Complete profile in one call' followed by an enumerated field list. It then establishes this as 'the canonical profile read' and explicitly contrasts with aliases, so an agent can distinguish it from sibling profile tools.

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?

The description explicitly names the profile/* alternatives (overview, details, about, education, skills, certifications, full-experience, social-matrix) and states they return the exact same record, telling an agent when this canonical tool is the right choice and that alternatives add no distinct value.

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