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tiagoyamashita

openlinkedinmcp

comment_on_post

Post a comment on a LinkedIn post by providing the post URL and comment text. Automates the submission through a real browser session.

Instructions

Comment on a LinkedIn post URL.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
commentYes
dry_runNo
post_urlYes
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. The description reveals nothing about side effects—whether posting a comment is visible publicly, whether it requires specific permissions, whether it can be undone, or what happens on failure/rate limits. The dry_run parameter hints at a testing capability but the description doesn't explain its behavior or consequences.

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?

Single sentence, zero wasted words. It is appropriately concise for the information it conveys, though it's under-specified rather than efficiently specified. The structure is clean but brevity is achieved through omission rather than thoughtful condensation.

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?

Despite having a 3-parameter schema with a non-obvious dry_run flag and no annotations or output schema, the description provides almost no context. For a mutation tool (commenting) with zero annotation coverage and zero schema description coverage, it should explain dry_run semantics, login requirements given sibling auth tools, and comment length/format expectations.

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 description coverage is 0%, so the description must compensate. It does not: none of the three parameters (post_url, comment, dry_run) are discussed. The dry_run parameter especially needs semantic explanation since its behavior is non-obvious from the schema alone. The description adds no value beyond the raw schema.

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 uses a specific verb+resource ('Comment on a LinkedIn post URL') which clearly states the action. However, it doesn't distinguish itself from sibling tools that could overlap like like_post or repost, and there's a note that it could relate to authorization requirements. The purpose is clear but minimal with no differentiation from siblings.

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 given on when to use this tool versus alternatives like like_post or repost. It doesn't mention prerequisites such as being logged in (linkedin_login) or having authorization, which is relevant given sibling auth tools exist (linkedin_auth_status, linkedin_login). The context is implied but no exclusions or prerequisites are stated.

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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