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BACH-AI-Tools

LinkedIn Data API MCP Server

get_article_comments

Retrieve comments from a LinkedIn article by providing its URL. Optionally specify page number and sort order to control output.

Instructions

Get article comments with url

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesExample value: https://www.linkedin.com/pulse/2024-corporate-climate-pivot-bill-gates-u89mc/?trackingId=V85mkekwT9KruOXln2gzIg%3D%3D
pageNoExample value: 1
sortNoExample value: REVERSE_CHRONOLOGICAL
Behavior2/5

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

No annotations are provided, so the description must disclose behavioral traits. It only states the action without mentioning pagination behavior, rate limits, or whether comments are returned in full. The schema hints at pagination via the 'page' parameter, but this is not clarified in the description.

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 ultra-concise with a single sentence. No fluff, but it may be too brief to fully inform the agent. Still, it is efficiently front-loaded.

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 the lack of output schema and annotations, the description does not explain what the tool returns (e.g., list of comments with details) or how it differs from related tools. Important context for an agent deciding between tools is missing.

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%, with example values for all three parameters. The description adds no additional meaning beyond what the schema provides, so a baseline score of 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 clearly states the verb 'Get' and resource 'article comments', and includes 'with url' indicating the primary parameter. However, it does not differentiate from sibling tools like get_article_reactions, which also retrieve feedback on articles.

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?

The description provides no guidance on when to use this tool versus alternatives such as get_article or get_article_reactions. There is no mention of prerequisites, fallbacks, 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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