ZOOQ - LinkedIn Data for AI Agents

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

Name	Required	Description	Default
`slug`	Yes	Company public slug — the part after linkedin.com/company/.

Output Schema

TableJSON Schema

Name	Required	Description
`id`	No	Example value was a string
`url`	No	Example value was a string
`name`	No	Example value was a string
`slug`	No	Example value was a string
`type`	No	Example value was a string
`hq_city`	No	Example value was a string
`website`	No	Example value was a string
`logo_url`	No	Example value was a string
`headcount`	No	Example value was a number
`industries`	No	Array in the example
`description`	No	Example value was a string
`headquarter`	No
`specialities`	No	Array in the example
`industries_v2`	No	Array in the example
`follower_count`	No	Example value was a number
`headcount_range`	No	Example value was a string
`hq_country_code`	No	Example value was a string

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 1, 2026

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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