ZOOQ - LinkedIn Data for AI Agents

profile_entity_id

Read-onlyIdempotent

Resolve a public handle to the person entityId used by the live person endpoints (posts, comments, interests, lookalikes). 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": {
      "handle": "<handle>"
    },
    "name": "profile_entity_id"
  }
}

Input Schema

Name	Required	Description	Default
`handle`	Yes	Public profile handle — the part after linkedin.com/in/. A full profile URL works too.

Output Schema

Name	Required	Description	Default
`handle`	No	Example value was a string
`entityId`	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 convey read-only, idempotent, and non-destructive behavior. The description adds valuable behavioral context beyond annotations by disclosing the 10-credit cost and the live nature of the resolved id, which helps an agent use the tool cost-effectively.

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?

Two short sentences convey purpose, scope, caching guidance, and cost with zero redundancy. The key outcome is front-loaded and every clause 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?

For a single-parameter lookup with a rich schema, supportive annotations, and an output schema, the description is complete. It states what the tool does, how to use the result, and the operational cost, leaving no critical gap for an agent to call it 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 input schema is fully covered at 100%, and the schema already explains what 'handle' means, including the URL variant. The description does not add meaning beyond the schema, so 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 specific verb ('Resolve') and states the exact resource ('public handle') and output ('person entityId') with the endpoints that consume it. It clearly distinguishes this from sibling lookups like companies_entity_id by specifying the live person endpoints.

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 description gives clear context: this is a prerequisite for live person endpoints and the id should be resolved once and reused. It does not explicitly name alternative lookups or state when not to use it, but the intended workflow is strongly implied.

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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