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ZOOQ - LinkedIn Data for AI Agents

by io.github.baldiga

companies_name_lookup

Read-onlyIdempotent

Search companies by name, with the full firmographic filter set. Cursor-paginated. Same upstream as search_companies — use whichever entry point reads better; they are equivalent. (Costs 10 Zooq credits.)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesCompany name (min 3 chars).
limitNoResults per page, 1-50 (default 20).
cursorNoOpaque pagination cursor; omit for the first page, then pass pagination.next_cursor from the previous response.
foundedNoFounding year filter.
hq_cityNoHQ city filter (min 3 chars).
websiteNoCompany website domain filter.
industriesNoIndustry name(s), comma-separated. Plain strings — this is the Data API, no id resolution needed.
industries_v2NoIndustry name(s) on the newer taxonomy, comma-separated.
hq_country_codeNoHQ ISO country code, e.g. us.
staff_count_maxNoMaximum employee count.
staff_count_minNoMinimum employee count.
follower_count_maxNoMaximum follower count.
follower_count_minNoMinimum follower count.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
itemsNoArray in the example

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnly, idempotent, and non-destructive behavior. The description adds non-obvious operational context beyond those annotations: cursor-based pagination, a 10-credit cost, and equivalence to another upstream endpoint. This is exactly the kind of additional behavioral detail that helps an agent invoke the tool correctly.

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 sentences with the main purpose front-loaded, followed by pagination, sibling equivalence, and cost in descending importance. There is no filler, 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?

With 13 parameters fully documented in the schema and an output schema present, the description only needs to cover disambiguation, operational quirks, and cost—all of which it does. An agent has everything necessary to select, call, and interpret this tool 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?

Schema coverage is 100%, and every parameter already has a clear description, so the baseline is 3. The description only mentions the filter set at a high level and does not add any parameter-specific syntax or meaning beyond what the schema already provides.

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 states a specific action ('Search companies by name') and a clear resource with a defined scope ('full firmographic filter set'). It also differentiates the tool from its sibling search_companies by explicitly declaring them equivalent, removing ambiguity about entry-point choice.

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

Usage Guidelines5/5

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

The description explicitly names search_companies as an alternative and says 'use whichever entry point reads better; they are equivalent.' This directly tells an agent when and how to choose between the two, which is the main sibling-confusion this tool could create.

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