Skip to main content
Glama

ZOOQ - LinkedIn Data for AI Agents

by io.github.baldiga

companies_posts

Read-onlyIdempotent

A company's recent posts. data.activities[].entityId is the activity id consumed by /posts/info, /posts/comments, /posts/likes. Keyed by the numeric organization id: pass slug and Zooq resolves it for you at no extra credit cost, or pass id from companies_entity_id to skip the lookup. (Costs 10 Zooq credits.)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idNoNumeric organization id from companies_entity_id; the urn:li:organization: form is accepted. An org_ id (dataset namespace, from companies_info) or a slug placed here is recognized and translated automatically. Provide `id` OR `slug`.
slugNoCompany public slug — the part after linkedin.com/company/ — or the full company URL. Resolved to `id` automatically at no extra credit cost. Any company identifier is accepted here and sorted by format (slug, URL, numeric id, org_ id). Provide `id` OR `slug`; `slug` is the simplest.
startNoPagination offset.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
activitiesNoArray 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 mark it read-only, idempotent, and non-destructive. The description adds meaningful behavioral context: credit cost, slug-to-id resolution cost behavior, and that data.activities[].entityId feeds /posts/info, /posts/comments, /posts/likes. 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 terse sentences, front-loaded with what the tool returns and immediately useful resolution/cost details. Each sentence earns its place, though the second sentence is dense with endpoint names.

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?

Together with output schema, annotations, and parameter descriptions, the tool definition covers return consumption, identifier formats, pagination (via start), and cost. It could be more explicit about default behavior when neither id nor slug is supplied, but the overall package is sufficient.

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?

Input schema covers 100% of parameters in detail; the description repeats the id/slug resolution guidance already present in the schema and adds only the credit-cost note. That is not enough to raise above the schema-covered baseline.

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 opening phrase and annotation title identify the resource and action ('recent posts' / 'List company posts'), and the mention of data.activities[].entityId links returned records to downstream post endpoints. It does not explicitly name sibling tools like posts_all or companies_info, so differentiation rests on 'company's recent posts' rather than an explicit contrast.

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?

The description gives concrete invocation context: pass slug (auto-resolved at no extra credit cost) or pass id from companies_entity_id to skip lookup, plus the 10-credit cost. It does not, however, state when to prefer this over alternatives such as posts_all, posts_info, or companies_entity_id, nor any exclusions.

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