Zooq

search_companies_live

Read-onlyIdempotent

LIVE company search. Its draw is hasJobs — an actively-hiring filter available nowhere else in the catalog — plus bucketed headcount search. For firmographic filtering (staff/follower counts, founded year, website) use search_companies instead. Offset-paginated. Not-found is free upstream. (Costs 10 Zooq credits.)

Input Schema

TableJSON Schema

Name	Required	Description
`count`	No	Results per page, 0-50 (default 25).
`start`	No	Pagination offset, 0-999.
`hasJobs`	No	Only companies with open postings — a hiring-intent signal. Accepted values: true, false.
`keyword`	Yes	Search keyword. Required by the upstream for this endpoint.
`industry`	No	Industry id(s), comma-separated. Takes an upstream id, NOT free text. No Zooq or upstream endpoint currently mints these ids, so free text is ignored (you get unfiltered results) — leave it unset until that changes. See ROADMAP F.
`geoEntityId`	No	HQ location filter. Geo entity id — this is LinkedIn's own public geo id, and it works. To find one: type your target city, state or country into the location box on LinkedIn search, pick the right match from the auto-complete dropdown, press enter to load the results page, then read `geoId=` followed by a long number out of the browser address bar (e.g. Israel = 101620260). Comma-separate for several. Resolve once and reuse it. Verified working on this endpoint. If you would rather filter by plain text, search_companies takes hq_city / hq_country_code instead.
`headcountRange`	No	Employee-count bucket. Accepted values: 1-10, 11-50, 51-200, 201-500, 501-1000, 1001-5000, 5001-10000, 10001+.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.5/5.0

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. The description adds valuable behavioral details beyond those: 'Offset-paginated,' 'Not-found is free upstream,' and 'Costs 10 Zooq credits.' This gives the agent insight into pagination and cost effects. It doesn't describe rate limits or data freshness nuances, but the most critical behavioral traits are disclosed.

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?

Each sentence earns its place: the first gives the core action, the second highlights the unique filter, the third routes to an alternative, and the final compact sentence covers pagination, not-found cost, and credit cost. The description is short, front-loaded, and free of filler.

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?

Although the tool has seven parameters, the schema fully documents them, and an output schema exists. The description covers when to use this vs. the sibling, how pagination works, and what the cost consequences are. Combined with the detailed annotations and schema, nothing critical is missing for correct invocation.

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 schema already explains all seven parameters, including accepted values and caveats. The description reinforces that hasJobs is the key differentiator and that headcount is bucketed, but it doesn't add new parameter-level meaning beyond the schema. This matches the baseline of 3 for high schema coverage.

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 'LIVE company search,' immediately stating verb and resource. It then differentiates itself from sibling search_companies by naming its unique hasJobs filter and bucketed headcount search, while explicitly excluding firmographic filtering. This gives an agent a precise, non-tautological understanding of the tool's role.

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 (when needing hasJobs or headcount buckets) and when not to (firmographic filtering), directing it to the correct sibling: 'For firmographic filtering... use search_companies instead.' It also mentions cost and pagination, giving practical invocation context.

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

All Zooq tools Inspect

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

A3.6/5.0

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

Need Help?