data_checkup

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

No annotations provided, so description carries full burden. It declares the tool is 'Read-only', which is a key behavioral trait. It lists the data aspects checked, implying no destructive actions. However, it does not describe return format or any side effects beyond read-only.

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?

Two sentences, no wasted words. First sentence states purpose, second provides usage guidelines. Front-loaded and highly efficient.

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 parameterless tool with no output schema, the description covers purpose, usage, and read-only nature. It lacks explicit mention of output type (e.g., a report or logs), but the tool's simplicity means this is a minor gap.

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?

Input schema has zero parameters (coverage 100%). With no parameters, baseline is 4. The description appropriately avoids parameter details since none exist, but it does not add any parameter information beyond the schema.

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 verb 'Run health checks' on 'the memory database' and lists specific aspects checked: vector dimensions, orphans, tier distribution, conflict backlog. It distinguishes from sibling tools like memory_drill_down or memory_stats by focusing on pre-consolidation diagnosis.

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?

Explicitly states 'Read-only' and specifies when to use: 'when diagnosing data quality issues or before a consolidation run.' This gives clear context and a concrete scenario, though it does not mention when not to use or alternatives.

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