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BACH-AI-Tools

LinkedIn Data API MCP Server

get_company_post_comments

Retrieve comments for a specific company LinkedIn post using its URN. Sort results by relevance or date and paginate through responses to analyze engagement and feedback.

Instructions

Get comments of a company post

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urnYesExample value: 7179144327430844416
sortYesExample value: mostRelevant
pageNoExample value: 1
Behavior1/5

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

No annotations provided. The description offers no behavioral information beyond the action. It does not mention rate limits, authentication needs, sorting behavior, pagination details, or any potential side effects.

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 very concise (4 words) and front-loaded. However, it is too terse and sacrifices necessary details for completeness. It could be improved by adding brief context on usage or parameters.

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 no output schema, no annotations, and an average complexity of 3 parameters, the description is incomplete. It fails to explain return format, pagination, sorting behavior, or any constraints, leaving the agent underinformed for correct invocation.

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 coverage is 100% with all three parameters described, albeit with only example values. The description adds no additional meaning beyond the schema. Baseline is 3 due to high coverage, but weak parameter descriptions prevent a higher score.

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 clearly states the action 'Get' and the resource 'comments of a company post'. It is specific enough to avoid confusion with generic comment retrievers, but it does not differentiate from siblings like 'get_profiles_comments' or 'get_post'.

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 on when to use this tool vs. alternatives. For example, one might use 'get_post' to retrieve comments along with the post, or 'get_profiles_comments' for a different user context. The description lacks any context about prerequisites or when not to use it.

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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