Skip to main content
Glama

ZOOQ - LinkedIn Data for AI Agents

profile_interests

Read-onlyIdempotent

Entities the person follows (companies, groups, people, newsletters). (Costs 10 Zooq credits.)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
handleNoPublic profile handle — the part after linkedin.com/in/ — or the full profile URL. Resolved to `entityId` automatically at no extra credit cost. Any person identifier is accepted here and sorted by format (handle, URL, ACoAA… entityId, prsn_ id). Provide `entityId` OR `handle`; `handle` is the simplest.
entityIdNoLive person entityId (ACoAA…) from profile_entity_id / profile_enrich; the urn:li:fsd_profile: form is accepted. A prsn_ id (dataset namespace, from profile_full) or a handle placed here is recognized and translated automatically. Provide `entityId` OR `handle`.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
interestsNoArray in the example

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already cover read-only, idempotent, and non-destructive behavior. The description adds a meaningful operational detail — the cost of 10 Zooq credits — which is not present in annotations and helps the agent weigh invocation cost. No contradiction with annotations exists.

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 one concise sentence plus a parenthetical cost note. It front-loads the core function and introduces no redundant or tangential information. Every element 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 read-only list operation with rich annotations, a detailed input schema, and an existing output schema, the description is complete. It covers the semantic core and the cost side effect, while parameters and return structure are handled elsewhere. Nothing an agent needs to call it correctly 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?

Both parameters are fully documented in the input schema with detailed descriptions of accepted formats (handle, entityId, resolution rules, and the OR condition). The description itself adds no parameter-specific information, which is acceptable because schema coverage is 100% and there is no gap for the description to compensate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the resource ('entities the person follows') and enumerates the entity types (companies, groups, people, newsletters), which distinguishes it from sibling tools like profile_full and profile_recommendations. It lacks an explicit verb such as 'get' or 'list', but the intent is unambiguous given the tool name and title.

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

Usage Guidelines3/5

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

The description does not explicitly state when to use this tool versus alternatives, nor does it mention exclusions or conditions. However, the phrase 'Entities the person follows' implies a clear use case: when you need the list of followed entities. This is adequate but relies on the agent to infer applicability.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.