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felipfr

LinkedIn MCP Server

by felipfr

comment_on_post

Add a meaningful comment to a LinkedIn post by specifying the post ID and comment text, facilitating engagement and interaction via the LinkedIn MCP Server.

Instructions

Add a thoughtful comment to a LinkedIn post

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
commentYesComment text
postIdYesLinkedIn post ID
Behavior2/5

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

With no annotations provided, the description carries full burden but only states the action without disclosing behavioral traits. It doesn't mention authentication requirements, rate limits, whether comments are editable/deletable, or any side effects. For a mutation tool with zero annotation coverage, this is a significant gap in transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, with every part earning its place by conveying the essential action and target.

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 this is a mutation tool with no annotations, no output schema, and 2 parameters, the description is incomplete. It lacks crucial context like authentication needs, response format, error handling, or interaction with sibling tools. For a tool that modifies data on a platform like LinkedIn, more completeness is needed.

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%, so the schema already documents both parameters ('postId' and 'comment'). The description adds no additional meaning beyond what the schema provides, such as format examples or constraints. Baseline 3 is appropriate when the schema does the heavy lifting.

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 ('Add a thoughtful comment') and target resource ('to a LinkedIn post'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'like_post' or 'share_post' beyond the core action, missing specific sibling distinction.

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 like 'like_post' or 'share_post', nor does it mention prerequisites such as authentication or post visibility. It lacks context about appropriate scenarios or exclusions, leaving usage entirely implicit.

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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