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ZOOQ - LinkedIn Data for AI Agents

by io.github.baldiga

companies_universal_name_to_id

Read-onlyIdempotent

Resolve a company slug (the part after linkedin.com/company/) to its stable org_ id — the dataset id used by /companies/info. For the live company endpoints (posts, similar, affiliated, insights) use companies_entity_id instead; the two ids are not interchangeable. Returns the FULL company record (identical to /companies/info) — read data.id. (Costs 10 Zooq credits.)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYesCompany public slug — the part after linkedin.com/company/.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idNoExample value was a string
urlNoExample value was a string
nameNoExample value was a string
slugNoExample value was a string
typeNoExample value was a string
hq_cityNoExample value was a string
websiteNoExample value was a string
logo_urlNoExample value was a string
headcountNoExample value was a number
industriesNoArray in the example
descriptionNoExample value was a string
headquarterNo
specialitiesNoArray in the example
industries_v2NoArray in the example
follower_countNoExample value was a number
headcount_rangeNoExample value was a string
hq_country_codeNoExample value was a string

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior, so the description correctly adds value beyond them: it discloses the 10-credit cost, states that the full company record is returned, and names the exact field to read (data.id). This goes beyond the structured metadata without contradicting it.

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?

Every sentence earns its place: the core resolution behavior, the critical sibling distinction, the return-value note, and the cost. The most important information is front-loaded in the first sentence, and there is no filler.

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 tool has only one parameter and an output schema, the description is complete: it explains the input, the output shape, the relevant field to read, and the credit cost. No critical behavioral or operational information is missing for an agent to call this 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?

The single parameter is already fully documented in the schema ('Company public slug — the part after linkedin.com/company/'), and the description repeats the same information without adding extra format rules, examples, or validation details. With 100% schema coverage, a 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 uses a clear verb ('Resolve') and resource ('company slug') to state exactly what the tool returns: a stable org_ id used by /companies/info. It also distinguishes this id from companies_entity_id, making the tool's purpose unambiguous relative to likely siblings.

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 the agent when to use this tool (slug → dataset id for /companies/info) and when to use an alternative (live company endpoints should use companies_entity_id instead). The warning that the two ids are not interchangeable removes a key source of misuse.

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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