Skip to main content
Glama

linkdapi-the-best-linkedin-professional-data-api

Reactions

get_api_v1_articles_article_reactions

Get article reactions Group: articles. Billing per call: 1 Credits.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urnNothe thread Urn get it from articles info endpoint
startNo

Schema Changelog

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

  1. First observed

TDQS

C2.9/5.0
Behavior2/5

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

Annotations are absent, so the description must carry the burden of behavioral disclosure. It only states the billing per call and implicitly that it's a GET request, but does not explain pagination (the 'start' parameter), response format, or whether any rate limits apply. The description is silent on these behavioral aspects.

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?

The description is extremely concise, consisting of two short sentences. While it is not bloated, it lacks substantive detail. It does include useful billing information, but the brevity comes at the cost of missing important context. It is efficient but under-informative.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given there is no output schema and only two parameters, the description should explain what the response contains, how pagination works, and any dependencies on other endpoints. None of this is covered. The tool appears simple, but the lack of return-value details and parameter usage makes it incomplete for a user to invoke correctly without external knowledge.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 50% (only 'urn' has a description). The description does not add any parameter semantics beyond what is in the schema. The 'start' parameter is undocumented in both the schema and description, leaving a gap that a good description should fill. It does not clarify how 'start' is used for pagination or offsets.

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 clearly states the action ('Get article reactions') and specifies the resource scope ('Group: articles'). This distinguishes it from other article-related tools like get_api_v1_articles_all and get_api_v1_articles_article_info, though it could be confused with reactions on other entities, the 'Group: articles' qualifier resolves that.

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?

No guidance is provided on when to use this tool versus alternatives like get_api_v1_posts_likes or get_api_v1_profile_reactions. There is no mention of prerequisites, such as needing to obtain the URN from the articles info endpoint, even though the parameter schema hints at this. The description offers no decision context.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

C2.7/5.0
Disambiguation3/5

Most tools target distinct resources (profiles, posts, companies, jobs), but there is notable overlap among profile-related endpoints (about, overview, details, full) and company insights vs. employees_data vs. insights. An agent could struggle to pick the right one without reading fine-grained descriptions.

Naming Consistency3/5

The naming follows a consistent snake_case pattern starting with 'get_api_v1_', making it predictable. However, there are typos ('siilar', 'campany'), mixed terms (lookup vs. search vs. get), and extremely long redundant prefixes that reduce clarity, though the overall style is uniform.

Tool Count2/5

With 50 tools, this is well above the 25-tool threshold, making the surface feel heavy and overwhelming. While the domain is broad (LinkedIn data), many endpoints could be consolidated (e.g., profile about/overview/details/full) to reduce the count without losing functionality.

Completeness4/5

For a read-only LinkedIn data API, the coverage is quite comprehensive: profiles, posts, companies, jobs, searches, geos, skills, and services are all represented. Obvious gaps are minimal—only a few advanced search filters or batch operations could be missing, but core data retrieval is well covered.

Resources