Skip to main content
Glama

ZOOQ - LinkedIn Data for AI Agents

by io.github.baldiga

search_companies

Read-onlyIdempotent

Search organizations by name or website with firmographic filters. Cursor-paginated. (Costs 10 Zooq credits.)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNoCompany name (min 3 chars). Provide name OR website.
limitNoResults per page, 1-50 (default 20).
cursorNoOpaque pagination cursor; omit for the first page.
foundedNoFounded year.
hq_cityNoHQ city filter.
websiteNoCompany website (min 3 chars). Provide name OR website.
industriesNoIndustry name(s) — pass plain strings, comma-separated.
industries_v2NoIndustry v2 taxonomy name(s), comma-separated.
hq_country_codeNoHQ ISO country code.
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

A3.6/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false). The description adds useful non-annotation-specific behavior: cursor pagination and a credit cost of 10, which helps the agent anticipate side effects and usage cost.

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 short sentences each earn their place: purpose, pagination behavior, and cost. The main action and scope are front-loaded, with no filler or repetition of schema details.

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 a fully documented 13-parameter schema and an output schema, the description supplies the key invocation-level facts: search action, pagination, and credit cost. The main gap is the missing dataset-vs-live distinction relative to search_companies_live, but that is more a tool-selection concern than an invocation gap.

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 baseline of 3 applies. The description only summarizes the parameters as 'name or website with firmographic filters' without adding detail beyond the schema. It does not need to compensate for gaps, and it doesn't.

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 uses a specific verb ('Search') and resource ('organizations') and names the scope: by name or website with firmographic filters. It does not explicitly differentiate from closely related siblings like search_companies_live, so it misses the final distinction point for a 5.

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

Usage Guidelines2/5

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

The description provides no when-to-use or when-not-to-use guidance and names no alternatives. Given overlapping siblings such as search_companies_live, companies_name_lookup, and companies_universal_name_to_id, an agent gets no help choosing among search entry points.

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