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Glama

ZOOQ - LinkedIn Data for AI Agents

by io.github.baldiga

posts_likes

Read-onlyIdempotent

People who reacted to a post + reaction type and total. (Costs 10 Zooq credits.)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
startNoPagination offset — MUST be a multiple of 10 (0, 10, 20, ...). Upstream pages by page number; the exact start-to-page mapping is still being verified.
entityIdYesActivity id — bare numeric or urn:li:activity: form, both accepted. Get it from companies_posts — read data.activities[].entityId (person feeds are currently unavailable).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNoExample value was a number
reactionsNoArray in the example
totalPagesNoExample value was a number
totalReactionsNoExample value was a number

Schema Changelog

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

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered structurally. The description adds valuable non-obvious behavior by warning that the call 'Costs 10 Zooq credits,' which annotations do not convey. 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single tight sentence that delivers the core behavior and the cost caveat with zero filler. It is front-loaded and every part 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?

Combining the description, annotations, and fully documented schema parameters, an agent has everything needed to invoke this read-only reactions list correctly: entity source, accepted id formats, pagination constraint, output shape via schema, and cost. There is no material missing context for this tool's complexity.

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% and the parameter descriptions are already detailed (entityId accepts bare or urn:li:activity forms; start must be a multiple of 10). The description adds no parameter-level meaning beyond the schema, so the baseline 3 is appropriate.

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 names the exact output ('People who reacted to a post + reaction type and total') and the annotation title adds the 'List' verb, making the operation clear. It is distinguishable from comment-focused siblings like posts_comments and comments_all because it focuses on reactions rather than comments.

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 itself does not name alternative tools, but the schema's entityId description provides strong operational guidance: get the id from companies_posts and use data.activities[].entityId, while noting person feeds are unavailable. The credit-cost warning also signals an important consideration before invoking the tool.

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.

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