google_trends_search: GET /

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

With no annotations provided, the description carries the full burden, and it does that well by explaining output: 'Returns interest-over-time series, geo-level breakdowns, and rising/top related topics/queries with relative scores.' This implies a read-only data retrieval operation. It omits rate limits, auth requirements, or failure behavior, but for this static trend-retrieval tool, the described behavior is adequate.

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 reasonably compact and front-loaded: a brief "Get Google Trends Data" heading, a clear summary of what the tool pulls, and the useful context about return type and best use cases. Each sentence adds useful information, although the use case enumeration could be shorter without losing much.

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 an eight-parameter tool with no output schema, the description provides solid high-level context: main parameters, supported data types, output types, and typical use cases. The details per parameter are already in the schema. The description does not cover exact multi-query syntax or what error responses might look like, but it gives enough for an agent to select and invoke the tool 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 description coverage is 100%, so each parameter already has a clear description, including enum options and examples. The natural-language description only repeats the parameter names at a high level and adds no new semantic detail. Baseline 3 is appropriate because the schema clearly does the heavy lifting.

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 opens with 'Pulls Google Trends data for one or more queries with geo targeting, region granularity, date range, category, time zone, Google property, and dataType.' This clearly names the action, resource, and main capabilities, so an agent can tell what the tool does without needing to infer from the schema.

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 explicit use cases: keyword/content research, demand forecasting, seasonality analysis, topic discovery, and campaign timing. It doesn't list exclusions or alternatives, but no sibling tools exist, so the usage guidance is strong. It would be even better if it stated when not to use the tool.

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