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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the agent knows it's a safe read. The description adds valuable context: it returns top time slots ranked by engagement score and falls back to industry defaults when insufficient personal data exists. This goes beyond the annotations and clarifies expected behavior.

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 exceptionally concise, only two sentences, and front-loads the main purpose in the first sentence. The second sentence adds necessary detail about output ranking and fallback. Every word earns its place, with no redundancy.

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 read tool with two well-described parameters and no output schema, the description covers the essential aspects: what the tool returns, the basis (engagement data), and the fallback behavior. It is sufficiently complete given the tool's low complexity and strong annotations, though it could mention the time slot format for extra clarity.

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%, with both platform and limit described in the input schema. The description adds no additional parameter-level detail beyond the schema, so a baseline score of 3 is appropriate. It does mention engagement data and fallback behavior, which indirectly relates to platform, but doesn't enrich parameter semantics.

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 function: to get optimal posting times for a platform based on historical engagement data. It uses a specific verb (get) and resource (optimal posting times), and the title reinforces the purpose. It distinguishes itself from general analytics and scheduling tools by focusing on best times.

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 the tool is for when you need optimal posting times based on engagement data, and mentions fallback to industry defaults. However, it does not explicitly contrast with sibling tools like 'suggest_next_schedule_time' or 'suggest_queue_slots', so the guidance remains implied rather than explicit.

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