Zooq

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

Name	Required	Description
`name`	Yes	Company name (min 3 chars).
`limit`	No	Results per page, 1-50 (default 20).
`cursor`	No	Opaque pagination cursor; omit for the first page, then pass pagination.next_cursor from the previous response.
`founded`	No	Founding year filter.
`hq_city`	No	HQ city filter (min 3 chars).
`website`	No	Company website domain filter.
`industries`	No	Industry name(s), comma-separated. Plain strings — this is the Data API, no id resolution needed.
`industries_v2`	No	Industry name(s) on the newer taxonomy, comma-separated.
`hq_country_code`	No	HQ ISO country code, e.g. us.
`staff_count_max`	No	Maximum employee count.
`staff_count_min`	No	Minimum employee count.
`follower_count_max`	No	Maximum follower count.
`follower_count_min`	No	Minimum follower count.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`items`	No	Array in the example

TDQS

A4.7/5.0

Behavior5/5

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

Beyond annotations, the description discloses cursor-based pagination, the same upstream data source as search_companies, and a cost of 10 Zooq credits. These are behavioral facts an agent needs to invoke the tool efficiently and anticipate side effects (billing).

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?

Three sentences, no filler. Each sentence earns its place: purpose, pagination, equivalence with search_companies, and cost. Information is front-loaded, and the most important guidance appears early.

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?

Given the rich schema, output schema, and read-only annotations, the description is complete. It adds the non-obvious details an agent needs—pagination, cost, and equivalence with search_companies—while leaving parameter mechanics to the schema.

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?

The input schema already documents all 13 parameters with 100% coverage, so the baseline is 3. The description adds only a general reference to the 'full firmographic filter set' without detailing parameters, which is acceptable because the schema carries the burden.

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 verb and resource: 'Search companies by name, with the full firmographic filter set.' It clearly distinguishes the tool from siblings by noting it is equivalent to search_companies, and the name itself signals the lookup behavior.

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 tells the agent when to use this tool, naming the alternative: 'Same upstream as search_companies — use whichever entry point reads better; they are equivalent.' This removes ambiguity between two near-identical entry points.

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

A3.6/5.0

Disambiguation2/5

Many tools have strongly overlapping purposes: companies_name_lookup is explicitly equivalent to search_companies, companies_enrich/companies_info/companies_universal_name_to_id all return company-profile data, and profile_full overlaps with profile_employment_history and profile_enrich. The descriptions are detailed, but an agent would still frequently have to choose between near-duplicate endpoints.

Naming Consistency4/5

Tool names mostly follow a predictable resource-prefixed snake_case pattern, such as companies_*, jobs_*, posts_*, profile_*, and search_*, which makes the set readable and groupable. Minor inconsistencies like jobs_details_v2, g_title_skills_lookup, and mixed noun suffixes (info/details/full/lookup) keep it from a perfect score.

Tool Count2/5

44 tools is well beyond the heavy 25+ band, and several tools appear to be different lookup modes or near-duplicates of the same underlying capability. The broad LinkedIn-style data domain explains much of the size, but the set still feels bloated rather than well-scoped.

Completeness4/5

The API covers the core read-only professional-data workflows well: people, companies, jobs, posts, comments, likes, email discovery/verification, schools, skills, and targeted searches. Minor gaps exist, such as some job filters being unusable and no direct exposure of certain profile alias endpoints, but agents can generally complete end-to-end workflows.

Resources

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