get_google_trends

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

The description discloses that the tool returns normalized interest scores (0-100) and gives example keywords, but it does not mention any behavioral traits such as rate limits, authentication requirements, or potential data limitations. Since no annotations are provided, the description carries the full burden, and while it provides some useful behavioral context, it lacks completeness.

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 two sentences plus a use-case list and examples. It is front-loaded with the core purpose, and every sentence adds value without unnecessary detail. Extremely concise for the information provided.

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 simple tool with two parameters and no output schema, the description covers the essential aspects: what it does, when to use it, and example usage. It could be slightly more complete by mentioning the default period (7 days) or that the output is an array of data points, but it is largely 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?

Despite the input schema having 0% description coverage (according to context signals), the description adds meaning by mentioning 'specified keywords over customizable time periods (7, 30, 90+ days)' and providing example keywords like 'bitcoin', 'Tesla earnings', 'inflation'. This helps the agent understand parameter usage beyond the schema's structural definitions.

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 'Get relative search interest data from Google Trends for specified keywords over customizable time periods' and mentions the output format (normalized 0-100 scores). It clearly distinguishes this tool from siblings like get_price_history or get_market_movers, which cover different domains.

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 description provides a detailed list of when to use the tool: 'Use this when asked about public interest trends, search volume comparisons, trending topics, brand awareness over time, seasonal patterns, or correlation between search interest and stock movements.' It does not explicitly state when not to use it, but the context is clear.

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