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ZOOQ - LinkedIn Data for AI Agents

by io.github.baldiga

comments_all

Read-onlyIdempotent

Comments authored by a person across posts. Cursor-paginated. (Costs 10 Zooq credits.)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
startNoPagination offset (alternative to cursor).
cursorNoOpaque pagination cursor from the previous response's nextCursor.
handleNoPublic profile handle — the part after linkedin.com/in/ — or the full profile URL. Resolved to `entityId` automatically at no extra credit cost. Any person identifier is accepted here and sorted by format (handle, URL, ACoAA… entityId, prsn_ id). Provide `entityId` OR `handle`; `handle` is the simplest.
entityIdNoLive person entityId (ACoAA…) from profile_entity_id / profile_enrich; the urn:li:fsd_profile: form is accepted. A prsn_ id (dataset namespace, from profile_full) or a handle placed here is recognized and translated automatically. Provide `entityId` OR `handle`.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
commentsNoArray in the example
nextCursorNoExample value was a string

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, open-world, and non-destructive behavior. The description adds valuable behavioral context beyond annotations by noting cursor-based pagination and the 10 Zooq credit cost, which are not captured in the structured metadata. This is useful supplementary transparency for an agent deciding whether to call the tool.

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?

The description is three short sentences with no wasted words. The core purpose is front-loaded, followed by the key behavioral note on pagination and the credit cost. Every phrase 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?

Given the rich input schema, output schema, and safety annotations, the description adds the only missing operational details: pagination style and credit cost. An agent has enough to select and invoke this tool correctly without further context.

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 parameters are fully documented in the input schema itself. The description adds no parameter-specific meaning beyond what the schema already provides, so the baseline score applies. No credit cost or behavior info is needed for the parameters since the schema covers them.

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 identifies the resource as comments authored by a person and scopes it across posts, which distinguishes it from a tool like posts_comments that would target comments on a single post. It lacks an explicit verb, though the title 'List a person's comments' supplies the action. Overall the purpose is clear and largely distinguishable from siblings.

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

Usage Guidelines3/5

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

The phrase 'authored by a person across posts' implies this tool is for retrieving a person's comments across multiple posts, but it does not explicitly state when to prefer it over alternatives such as posts_comments. No exclusions or when-not-to-use guidance is provided. Usage context is implied rather than explicit.

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