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ZOOQ - LinkedIn Data for AI Agents

by io.github.baldiga

profile_recommendations

Read-onlyIdempotent

Recommendations written for the person, with author details and text. (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
endorsementsNoArray in the example

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A3.7/5.0
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, so the description does not need to repeat those. The description adds meaningful context beyond annotations by warning of the 10-Zooq-credit cost and clarifying the returned content ('author details and text'). This is useful behavioral information, though it stops short of describing any limits, pagination, or failure behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is short and front-loaded, stating the core purpose first and the credit cost in a parenthetical second. Every word earns its place, and there is no redundant restating of the schema. It could be slightly improved by leading with a verb like 'Get' or 'Returns', but it is otherwise efficiently written.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a low-complexity tool with no required parameters, a detailed input schema, and a present output schema, the description provides the essential context: who the data is about and what it costs. The credit cost is an important operational detail that is not in the schema or annotations. It does not explain when to use the tool versus siblings, but that gap is already reflected in the usage_guidelines score.

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 handle and entityId both documented in detail including format, resolution to entityId, and accepted variants. The tool description adds no parameter-specific semantics beyond reinforcing that the recommendations belong to 'the person'. With full schema coverage, the baseline score of 3 is appropriate.

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 names the resource ('recommendations'), the subject ('the person'), and the expected content ('author details and text'), so an agent can tell what data to expect. It lacks an explicit verb such as 'get' or 'returns', though the annotation title 'Get profile recommendations' reinforces the intent. Among the profile_* siblings, none is specifically dedicated to recommendations, so the tool is reasonably distinguishable.

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 implies the tool is used when recommendations for a person are needed, but it never explicitly states when to prefer it over related profile tools like profile_enrich, profile_full, or profile_interests. No alternatives, exclusions, or preconditions are mentioned. Because the name and description make the basic use case inferable, this is adequate but not explicit guidance.

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.

Resources