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

The description discloses key behavioral traits about the output content, such as it being a 'computed' summary with 'projections' and 'trends,' which is functional context not covered by the annotations. Since annotations already confirm it is read-only, the description adds valuable behavioral context about the data itself.

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 definition is concise, using two well-structured sentences. It front-loads the functional description before providing the usage context, maximizing clarity with minimal words.

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?

Given the relative simplicity of the schema (only two parameters), the detailed description of the output content and specific use cases provides maximum contextual completeness for a reasoning agent.

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 input schema provides 100% description coverage for both parameters. The description does not add any necessary meaning or syntax clarification to the inputs themselves, relying instead on the schema for parameter definition.

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 explicitly states that it gets a computed summary including follower projections and engagement trends, distinguishing it from simple static stats retrieval. Although the sibling 'get_growth_summary' exists, the detailed contents make the purpose clear.

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 usage guideline is highly explicit, telling the AI exactly when to select this tool (e.g., when asking about 'growth trajectory' or 'future estimates'). This is the strongest part of the definition and eliminates ambiguity.

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