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

The description goes far beyond the readOnly/destructive annotations, explaining critical interpretation behaviors: unsupported metrics are stored as 0 and must be reported as unavailable, linkClicks are tracked by BulkPublish and must not be summed with platform clicks, and bot/link-preview traffic is excluded. It also provides per-platform metric availability details, giving the agent essential context for interpreting results correctly.

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 lengthy but every sentence adds essential information for correct invocation and result interpretation. It is well-structured: purpose first, then critical caveat about supportedMetrics, then linkClicks semantics, then usage trigger. The IMPORTANT flag and clear segmentation prevent the density from becoming unwieldy.

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?

With no output schema, the description does an excellent job of explaining return semantics: per-platform metric availability, zero-value handling, linkClicks placement outside `latest`, and total aggregation. It gives the agent everything needed to interpret the response correctly despite the absence of a structured output definition.

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?

The schema covers 100% of the single parameter with 'The post ID to get metrics for,' so the description does not need to add much. It only clarifies that the post should be published, which is a marginal semantic addition. Baseline 3 is appropriate when the schema carries the documentation burden.

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 opens with a specific verb and resource: 'Get engagement metrics for a published post,' then enumerates the concrete metric types returned. It clearly distinguishes this tool from broader analytics or post-retrieval siblings by focusing on per-post engagement metrics.

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 explicitly states when to use the tool: 'Use this when the user asks how a specific post performed (likes, views, engagement).' However, it does not explicitly contrast this with alternative analytics tools like get_analytics or view_analytics, so it stops short of full when-not-to-use guidance.

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