ZOOQ - LinkedIn Data for AI Agents

companies_entity_id

Read-onlyIdempotent

Resolve a company slug to the numeric organization id used by the live company endpoints (posts, similar, affiliated, insights). Resolve once, reuse the id. (Costs 10 Zooq credits.)

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "slug": "<slug>"
    },
    "name": "companies_entity_id"
  }
}

Input Schema

Name	Required	Description	Default
`slug`	Yes	Company public slug — the part after linkedin.com/company/. A full company URL works too.

Output Schema

Name	Required	Description	Default
`id`	No	Example value was a number
`slug`	No	Example value was a string

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 1, 2026

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, covering the safety profile. The description adds transparency about the cost (10 Zooq credits) and reinforces the reusable nature of the result, which goes beyond the structured 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?

The description is compact and purposeful: it states the action and output, gives usage guidance, and discloses cost — all in two sentences with zero filler. The primary purpose is front-loaded.

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 single fully-documented parameter, an output schema present, and strong annotations, the description provides all necessary context: what the tool does, how to use the result, and cost. Nothing essential for correct invocation 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%, with the slug parameter already well-documented in the input schema. The description simply restates that a slug resolves to an id, adding no new parameter-level detail beyond the schema. 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 specific verb ('Resolve') and resource ('company slug to the numeric organization id'), and further distinguishes itself by stating the id is used by live company endpoints (posts, similar, affiliated, insights). This leaves no ambiguity about what the tool does or how it relates to sibling lookup tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The instruction 'Resolve once, reuse the id' provides clear operational guidance, and the mention of live endpoints implies when this id is needed. However, the description does not explicitly name alternative tools or state when not to use this tool, so it falls short of a full 5.

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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