zernio_get_linkedin_analytics

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

No annotations are provided, so the description carries full burden. It only lists metrics but does not disclose behavioral traits like read-only nature, required permissions, data freshness, or error conditions. The description adds minimal behavioral context beyond the purpose.

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 a single, front-loaded sentence that efficiently states the tool's purpose and key output metrics. No extraneous words or filler. It is appropriately concise.

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?

For a simple get-tool with three parameters and no output schema, the description is adequate but could include more context such as the type of return (e.g., JSON object), typical use cases, or limitations. The description covers the 'what' but not the 'how' or 'when', leaving some gaps.

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?

Schema coverage is 100%, so the input schema already describes all three parameters. The description does not add meaning beyond what the schema provides; it merely lists the output metrics. Therefore, the description adds no extra parameter-level value, justifying a baseline score of 3.

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 states it retrieves 'account-level aggregate analytics' and lists specific metrics (impressions, clicks, engagement rate, follower growth). This clearly distinguishes it from sibling tools like get_linkedin_post_analytics, which are post-level, and other analytics tools for different platforms.

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 implies usage for aggregate LinkedIn account analytics but does not explicitly state when to use or not use this tool. It does not mention alternatives such as post-level analytics or other LinkedIn-specific tools. The context is clear but lacks exclusionary guidance.

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