prune_database

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: it removes low-value rows based on specific criteria (zero clicks, low impressions), preserves recent and actionable data, runs VACUUM to reclaim disk space, and mentions the automatic execution after syncs. However, it lacks details on permissions, rate limits, or error handling, which are common gaps for mutation tools.

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 appropriately sized and front-loaded, starting with the core action and resource. Each sentence adds value: the first explains the pruning logic, the second covers VACUUM and automation, and the third provides usage guidance. There is no redundant or wasted information, making it efficient and well-structured.

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 complexity of a mutation tool with no annotations and no output schema, the description is fairly complete. It covers the tool's purpose, behavioral traits, and usage guidelines. However, it lacks details on output format or error responses, which would be helpful for an agent invoking the tool, slightly reducing completeness.

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 the schema already documents all parameters thoroughly. The description adds minimal semantic context beyond the schema, such as implying the purpose of parameters in the retention policy (e.g., 'older data' relates to recentDays), but it doesn't provide significant additional meaning or examples. This meets the baseline of 3 when schema coverage is high.

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 specific action ('Apply data retention policy', 'Removes low-value rows') and resource ('synced property database'), distinguishing it from siblings like sync_gsc_data or query_gsc_data by focusing on cleanup rather than data retrieval or synchronization. It explicitly mentions the tool's unique function of pruning based on criteria like zero clicks and low impressions.

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 guidance on when to use this tool vs. alternatives: it states 'Use preview_prune first to see what would be deleted,' indicating a clear alternative for previewing deletions. It also notes 'This runs automatically after each sync, but you can also trigger it manually,' clarifying the context for manual invocation versus automated execution.

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