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

Annotations already mark readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds value beyond these: it specifies the 0-100 scale, availability of absolute volume, and the plan-limit message on rate limiting/quota exhaustion. It also clarifies the keyword format constraints for special sources, which is behavioral context not in annotations.

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 efficient, with every sentence serving a purpose: it defines the tool, explains the value scale, differentiates from siblings, clarifies special source keyword rules, and discloses rate-limit behavior. It is well-structured, front-loading the core purpose and then addressing edge cases. No filler or redundant text.

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 tool's moderate complexity (3 params, all documented), no output schema, and rich annotations, the description is complete. It covers the return scale, absolute volume, plan-limit errors, source-specific keyword requirements, and the exact preset window list (implicitly via schema but reinforced). An agent has everything needed to call it correctly.

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 detailed descriptions, so baseline is 3. The tool description adds substantial meaning: it explains the source-specific keyword formats (npm exact package names, PyPI exact names, Steam display names, Android bundle IDs), provides concrete examples and wrong usages, and clarifies the rule when mixing app sources with others. This goes well beyond the schema's generic descriptions.

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 computes point-to-point growth for a keyword on one or more sources, with specific details on value scale (0-100) and absolute volume. It explicitly distinguishes from siblings: get_time_series for charting/custom math and get_top_trends for live boards, so an agent can immediately identify the right tool.

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 gives explicit when-to-use guidance: 'Designed for growth questions' and directs to get_time_series for charting or custom math. It also clarifies that app downloads/rankings are not the live boards on get_top_trends, preventing confusion. Rate-limit and quota behavior is disclosed, which is operational guidance.

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