Post's Comments
get_api_v1_posts_commentsget all comments for a given post Group: Posts. Billing per call: 1 Credits.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| urn | No | ||
| count | No | ||
| start | No | ||
| cursor | No |
get_api_v1_posts_commentsget all comments for a given post Group: Posts. Billing per call: 1 Credits.
| Name | Required | Description | Default |
|---|---|---|---|
| urn | No | ||
| count | No | ||
| start | No | ||
| cursor | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states 'get all comments' but does not reveal that pagination is likely via cursor/start/count, nor any authentication requirements, rate limits, or side effects. The statement 'get all' could mislead an agent into expecting a single full response without pagination.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded, stating the core function in the first sentence. It avoids unnecessary filler. However, it may be too brief given the tool's complexity, but for conciseness alone it earns a high score.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has four parameters, no output schema, and no annotations, the description is inadequate. It fails to explain pagination, post identification, or return structure. The tool appears simple but likely involves cursor-based pagination, which is not addressed, leaving the agent under-informed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning the description must compensate by explaining parameters. However, it does not mention any of the four parameters (urn, count, start, cursor) or their roles. The agent is left without guidance on which parameter identifies the post or how pagination works, rendering the tool difficult to use correctly.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool fetches all comments for a given post, which distinguishes it from sibling tools like get_api_v1_comments_all (all comments) and get_api_v1_posts_all (posts). The title 'Post's Comments' reinforces the purpose, making the primary function unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no explicit guidance on when to use this tool versus alternatives. The description does not mention exclusions, prerequisites, or scenarios where other comment-related tools might be preferred. The 'for a given post' phrase implies usage context but does not instruct the agent on selection criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.