Skip to main content
Glama

ZOOQ - LinkedIn Data for AI Agents

by io.github.baldiga

g_title_skills_lookup

Read-onlyIdempotent

Skill catalog search by name (partial match) — skills only, despite the endpoint name. Page-paginated. Use to find a skill's skl_ id or normalized_name for the /search/people skills filter. (Costs 10 Zooq credits.)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesSkill name (min 3 chars).
pageNoPage number, >=1 (default 1).
limitNoResults per page, 1-50 (default 20).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
itemsNoArray in the example

TDQS

A4.6/5.0
Behavior5/5

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

Beyond the readOnly/idempotent annotations, the description adds meaningful behaviors: partial matching, page-paginated results, skill-only scope despite the endpoint name, and a 10-Zooq-credit cost. No contradiction with 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?

Four short units of information—operation, pagination, use case, cost—are front-loaded and every clause earns its place. No filler or restatement of the schema.

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?

Given output schema, annotations, and full parameter coverage, the description supplies nearly everything needed: purpose, partial-match semantics, pagination, cost, and result usage. It could be more complete by explicitly distinguishing from g_skill_lookup, but nothing essential is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the schema already documents name, page, and limit. The description adds semantic value by explaining that name is a partial match and that the result supplies skl_ id/normalized_name for downstream filtering, going beyond the baseline.

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 opens with a specific verb-and-resource statement: 'Skill catalog search by name (partial match)' and clarifies scope ('skills only, despite the endpoint name'). It also states the concrete downstream purpose: finding skl_ id or normalized_name for the /search/people skills filter. This is enough to distinguish it from 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?

It explicitly says when to use the tool: to locate a skill's skl_ id or normalized_name for the /search/people filter. It does not name alternatives such as g_skill_lookup or state when not to use it, so it misses the exclusion part of ideal guidance.

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

All ZOOQ - LinkedIn Data for AI Agents toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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