gsc_new_keywords

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

With no annotations, the description carries the full burden. It discloses important behavioral expectations for the agent, such as 'Base your analysis ONLY on the data returned,' 'Report exact numbers,' and 'Do not speculate about causes.' However, it does not describe the tool's own operational traits (e.g., read-only nature, output fields, or potential limitations like pagination). The focus is on agent conduct rather than tool behavior.

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 longer than minimal but well-structured, with a clear purpose statement, an 'IMPORTANT' section for analysis rules, and a 'PRESENTATION' section for output requirements. While verbose, each section serves a distinct purpose, and it is front-loaded with the essential purpose. It could be slightly more concise, but the instructions are value-adding.

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?

Despite having no output schema, the description compensates by providing thorough guidance on how to handle and present results. It implies the data includes metrics like severity and change direction through the presentation instructions. However, it does not explicitly list the returned fields (e.g., impressions, clicks, position), leaving some ambiguity about the exact data structure. Overall, it covers purpose, usage, analysis constraints, and presentation, making it fairly complete for a complex tool.

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 input schema already provides 100% description coverage for all four parameters, so the baseline is 3. The description adds some context by indirectly referencing 'recent data' and 'baseline period' which map to recent_days and baseline_days, but it does not add new meaning beyond the schema or clarify parameter usage further.

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's function: 'Discover queries that appeared in your recent data but were not present in the baseline period.' This is a specific verb (Discover) with a resource (queries) and a comparative scope, which distinguishes it from sibling tools like gsc_content_gaps or gsc_quick_wins that likely focus on different patterns.

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 clear use cases: 'spotting new ranking opportunities, trending topics, or the impact of recently published content.' It gives context on when to use the tool but does not explicitly compare with alternatives or state exclusions, so it falls short of a 5.

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