data_checkup

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

With no annotations, the description explicitly states the tool is read-only and lists the checks performed. However, it does not describe the output format or error handling, but the read-only nature is clearly communicated.

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 two sentences, concise, front-loaded with the action, and contains no redundant information. Every part adds value.

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?

The description covers purpose, usage, and read-only nature. It lacks details about output format or how results are presented, but for a health-check tool with no output schema, this is acceptable. Could be slightly more complete.

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 tool has no parameters, so the description naturally does not add parameter semantics. Baseline for zero-parameter tools is 4, and the description is sufficient.

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?

Description clearly states the tool runs health checks on the memory database, listing specific aspects (vector dimensions, orphans, tier distribution, conflict backlog) and explicitly indicates it's read-only. This distinguishes it from mutation tools like batch_store or forget_memory.

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?

Description explicitly states when to use: 'diagnosing data quality issues or before a consolidation run,' and notes it is read-only, providing clear guidance on appropriate contexts.

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