All Comments
get_api_v1_comments_allGet all comments of a profile but its URN Group: comments. Billing per call: 1 Credits.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| urn | No | ||
| cursor | No |
linkdapi-the-best-linkedin-professional-data-api
by com.jojapi
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- First observed
TDQS
C2.4/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description mentions billing cost (1 credit) but does not disclose other behavioral traits such as read-only nature, rate limits, or side effects. With no annotations, more transparency is expected.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise (one sentence) but contains awkward phrasing and a typo, hindering clarity. It is not well-structured for the intended meaning.
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?
No output format, pagination behavior, or detailed use case is described. The description is skeletal and does not adequately support an agent in understanding the tool's full behavior.
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?
The description partially explains 'urn' as identifying the profile but leaves 'cursor' entirely unexplained. Since schema coverage is 0%, the description should clarify both parameters, but it falls short.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool fetches all comments for a profile identified by URN, but the phrasing 'but its URN Group: comments' is confusing and contains a likely typo. It conveys the main action but lacks clarity.
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 over alternatives (e.g., get_api_v1_posts_comments or get_api_v1_comments_likes). The description lacks usage context or prerequisites.
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
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.