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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, not open-world). The description adds real behavioral context beyond them: the data is snapshot-based rather than live, and only follower count has history, which sets expectations about freshness and coverage.

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?

Two sentences, zero padding, with the core purpose front-loaded and the data-provenance caveat second. Every clause carries information.

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?

No output schema exists, so the description could have sketched the return shape (e.g. per-account rows plus a total). It partially does this ('per account and in total') and explains history limits, making it adequate but not fully self-sufficient.

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 description coverage is 100%, including the 365-day history limit and the account/workspace resolution rules, so the schema does the heavy lifting. The description adds only the metric-level framing, not parameter syntax or semantics, so baseline 3 applies.

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?

States a specific verb+resource ('follower evolution per account and in total') and clarifies the data source (daily snapshots Ignix stores). It implicitly separates itself from live-metric siblings like get_engagement_trend, but never names an alternative, so it stops short of a 5.

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?

Usage is implied rather than stated: the note that follower count is the only metric accumulated over time tells the agent to come here for historical follower data and elsewhere for live metrics. There is no explicit when-to-use, when-not, or named alternative call.

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