create_comment

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 effectively describes authentication requirements, API product dependencies, permission constraints, and specific behavioral rules about image attachments in nested vs. top-level comments. It also mentions the return value format. The only gap is lack of rate limit or quota information.

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 well-structured with clear sections (purpose, requirements, args, returns, notes) and every sentence adds value. It could be slightly more concise by combining some of the LinkedIn API product notes, but overall it's efficiently organized with no wasted text.

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 complexity of LinkedIn API integration, 4 parameters with 0% schema coverage, no annotations, but with output schema present, the description provides comprehensive context. It covers prerequisites, constraints, parameter details, return values, and important behavioral notes about the LinkedIn platform's limitations. The output schema handles return value details, allowing the description to focus on operational context.

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?

With 0% schema description coverage, the description fully compensates by providing detailed parameter semantics. It explains each parameter's purpose, format examples for post_urn, character limits for text, optional nature of parent_comment_urn, and comprehensive options for image_path including local files, URLs, and base64 encoding. This adds significant value beyond the bare schema.

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's purpose with specific verb ('Create a comment') and resource ('on a LinkedIn post using the Official API'). It distinguishes from sibling tools like 'create_post' by focusing specifically on commenting rather than post creation, and from 'create_reaction' by specifying comment creation rather than reactions.

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?

The description provides explicit guidance on when to use this tool, including prerequisites ('Requires "Share on LinkedIn" product enabled'), alternative products needed ('Community Management API'), and specific constraints ('LinkedIn only allows images in nested comments... not in top-level comments'). It also mentions permission errors and when to seek alternative access.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.