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ZOOQ - LinkedIn Data for AI Agents

by io.github.baldiga

search_alumni

Read-onlyIdempotent

Alumni and current students of an institution (professional records + the education link). Page-paginated. Built for recruiting and warm-intro sourcing. (Costs 10 Zooq credits.)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNoPage number (>=1, default 1).
sortNoOrdering. Accepted values: newest, oldest, recently_graduated.
limitNoResults per page, 1-50 (default 20).
degreeNoDegree filter (min 3 chars), e.g. mba.
geo_cityNoCity filter (min 3 chars).
current_onlyNoRestrict to people currently studying there.
end_year_maxNoLatest graduation year.
end_year_minNoEarliest graduation year.
field_of_studyNoField-of-study filter (min 3 chars), e.g. computer science.
start_year_maxNoLatest enrollment year (>= start_year_min).
start_year_minNoEarliest enrollment year (1900-current+10).
normalized_nameYesThe institution's normalized name (lowercase, hyphenated). Discover it via search_schools — read data[].normalized_name.
geo_country_codeNoISO country code filter, e.g. us.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
itemsNoArray in the example

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already convey read-only/idempotent/non-destructive behavior. The description adds useful behavioral context beyond annotations: results are page-paginated, each call costs 10 Zooq credits, and records are professional plus education-linked. 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.

Conciseness4/5

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

Three short sentences, each carrying useful information: data scope, pagination, and use case/cost. It is efficient and front-loaded, though the phrase 'professional records + the education link' is slightly cryptic.

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 search tool with rich schema descriptions, an output schema, and safety annotations, the description adds the missing contextual pieces: pagination behavior, cost, and intended recruiting use. It is complete enough for an agent to invoke correctly, though it could name related tools.

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%, so the description does not need to repeat parameters. It adds only the generic 'page-paginated' hint, which is already implied by the page/limit parameters; no additional semantic value beyond the schema.

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 identifies the target data (alumni and current students of an institution) and the intent (recruiting and warm-intro sourcing), which makes the tool's purpose clear. It does not explicitly say 'search' or name sibling alternatives, so it lacks explicit differentiation from generic people-search 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 provides clear context: use this when sourcing alumni/current students for recruiting or warm intros. It does not state exclusions or point to alternatives such as search_people, so it falls short of full when-to-use/when-not-to-use 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