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Eyalm321
by Eyalm321

zernio_get_linkedin_post_reactions

Retrieve a breakdown of LinkedIn post reactions, counting likes, celebrates, support, love, insightful, and curious responses.

Instructions

Get reactions breakdown for LinkedIn posts — like, celebrate, support, love, insightful, curious counts.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
accountIdYesThe Zernio LinkedIn account ID
postIdNoSpecific post ID to get reactions for
Behavior2/5

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

With no annotations, the description should disclose that this is a read-only operation and any side effects. It does not state that it does not modify the post or require specific permissions, leaving ambiguity for the agent.

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?

A single sentence of 16 words that efficiently conveys the tool's purpose without any fluff. It is well-structured with a dash separating action from details.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

While the description covers the tool's purpose and mentions reaction types, it lacks details about the return format (e.g., counts per type) and does not mention pagination or limits. Given no output schema, this is a noticeable gap.

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?

The input schema provides descriptions for both parameters with 100% coverage. The tool description adds value by listing expected reaction types, but this is not essential for parameter usage.

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 the resource 'reactions breakdown for LinkedIn posts', listing specific reaction types. However, it does not explicitly distinguish from the sibling tool 'zernio_get_linkedin_post_analytics', which could cause confusion.

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 provided on when to use this tool versus alternatives (e.g., zernio_get_linkedin_post_analytics) or any prerequisites like authentication or account setup.

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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