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

With no annotations, the description carries full burden. It discloses output behavior (ranking relative to strongest region), granularity (daily/weekly/hourly based on timeframe), and pricing ($0.03/call). It doesn't mention error behavior or read-only nature, but the core behavioral traits are covered.

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 dense but well-organized: purpose, use case, price, then parameters with example. Every sentence adds value, though the timeframe enumeration in parentheses is a bit cluttered. It's front-loaded with the core purpose and scoping.

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 generic schema and lack of output schema, the description must cover parameters, output semantics, and usage context. It does so thoroughly, including granularity and pricing. Minor gaps: no error handling or edge cases, but for a read-only data tool this is acceptable.

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 schema only defines a generic 'params' object with additionalProperties true (0% coverage). The description fully compensates by explaining each parameter: keyword (search term), geo (ISO-3166 alpha-2 code, 213 countries), timeframe (with allowed values), and provides an example. This is essential for correct invocation.

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?

Clearly states the tool returns subregional breakdown of Google Trends interest for a keyword, with specific details on ranking (0-100 relative to strongest region). This distinguishes it from siblings like 'trend' (likely national) and related_queries/topics, making the 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?

Explicitly gives use cases: 'regional demand mapping, geo targeting, and finding where a keyword over-indexes before spending on it.' It implies when to use (need regional breakdown) but doesn't explicitly contrast with alternative tools like 'trend' for national-level data. Clear context, no exclusions.

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