keyword_trends

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

With no annotations, the description carries full burden. It discloses data source (Google Trends), cost (~1.5 credits, ~5s), and return data structure. It also provides tips on parameter usage. However, it does not explicitly state whether the operation is read-only or any authentication requirements, which are minor gaps.

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 well-structured with clear sections (introduction, use when, don't use, returns, pair with, cost, tips) and front-loaded with core purpose. While slightly verbose, every sentence adds value, and the structure aids readability. Could be tightened slightly, but effective.

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 complexity (4 parameters, no output schema) and many siblings, the description is comprehensive. It covers input constraints (max 5 keywords), parameter defaults, return data structure, cost, and pairing suggestions. It effectively informs the agent about what the tool does, how to use it, and its place among siblings.

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 each parameter having a description. The description adds significant value beyond the schema: examples for 'keywords' (e.g., ['wireless earbuds', 'bluetooth earbuds']), allowed values for 'timeRange' with context (e.g., 'today 12-m' for balanced trend), and guidance for 'region' and 'language'. This enriches the semantic understanding.

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 provides keyword trend data from Google Trends, including time series, geo heat, and related queries. It lists specific use cases like 'X关键词最近热度怎么样' and distinguishes from siblings like search_amazon for product searches. The verb '提供' (provides) and resource '关键词趋势' (keyword trends) are specific and 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?

The description explicitly provides 'Use when' and 'Don't use' sections with concrete examples, such as avoiding use for absolute search volume or product links. It also suggests pairing with other tools like search_amazon and filter_niches, offering clear context for when to use this tool versus alternatives.

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