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

Annotations already cover read-only and non-destructive nature, so the description correctly avoids redundancy. It adds valuable behavioral details beyond annotations: the aggregation semantics (followers as latest count, other metrics as sums over published posts, null for unsupported platforms) and the output shape with one point per bucket. This is meaningful context that helps an agent interpret 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 a single, well-structured paragraph that front-loads the core purpose (metrics and granularity), then explains the return format and aggregation rules, and concludes with sibling routing. Every sentence contributes essential information with no fluff or repetition. It is detailed yet efficiently organized.

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 read-only analytics tool with an output schema and full parameter documentation, the description covers all critical operational details: what metrics are returned, how they are aggregated, the null behavior, and how it fits with sibling tools. An agent has everything needed to invoke it correctly and interpret the response.

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 each parameter having a descriptive explanation. The description itself does not add significant parameter-level meaning beyond the schema; it only references granularity implicitly via 'bucketed by day (default), week or month' which is already in the schema. The baseline of 3 is appropriate given high schema coverage.

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 returns time-bucketed metrics (views, likes, comments, shares, followers, posts published, engagement rate) for trend analysis, with explicit granularity options. It distinguishes itself from siblings by naming get_analytics_overview for totals and list_post_analytics for post-level spikes, making its unique purpose unambiguous.

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?

It explicitly states the intended use case ('for trend questions such as...') and provides clear guidance on when to use alternatives ('Use get_analytics_overview for totals and list_post_analytics to see which posts drove a spike'). This gives an agent precise decision criteria for tool selection.

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