analyze_table_bloat

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

No annotations provided, so the description must disclose behavioral traits. It mentions what metrics are checked but does not state whether the tool is read-only, requires specific permissions, or has side effects. For a diagnostic tool, it omits important safety information.

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?

A single sentence that efficiently conveys the tool's purpose and scope. No redundancy, but could benefit from clearer separation of the separate metrics. Still, it is well front-loaded.

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?

With only two simple parameters and no output schema, the description explains what the tool checks but omits details about the return format, supported databases beyond mentioning Postgres and MySQL, and potential errors. Adequate but not fully 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?

Schema coverage is 100% (both parameters have descriptions). The description adds no additional meaning beyond the schema's parameter descriptions. Baseline score of 3 is appropriate as the schema already documents parameters adequately.

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 analyzes table bloat by checking specific metrics (dead tuple ratios, InnoDB fragmentation, vacuum history, table sizes). It includes database-specific details but does not explicitly differentiate from siblings like analyze_vacuum, which may have overlapping vacuum history checks.

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?

No explicit guidance on when to use this tool versus alternatives. The description implies it targets PostgreSQL and MySQL, but does not state prerequisites or scenarios where it is preferred over sibling tools like analyze_vacuum or inspect_schema.

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