table_bloat

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

With no annotations, the description bears full burden. It marks the tool as [READ] but provides no additional behavioral context (e.g., no side effects, permissions, or error handling). The formula and ordering are disclosed, but nothing about data freshness or empty results.

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 extremely concise: one line for purpose, then parameter docs. Front-loaded with the key purpose. No unnecessary words.

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 adequately conveys the tool's function for selection, but lacks details about return format (e.g., columns, data types) and edge cases. Given no output schema, a bit more context on what is returned would improve 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?

The schema has 0% description coverage, so the description must explain parameters. It does so clearly: 'limit' is number of tables to inspect with default 50, 'target' is target name from config with null default. This adds value beyond the schema's type/default info.

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 it reads dead-tuple bloat proxy per table, calculated as dead/(live+dead), sorted worst first. This distinguishes it from sibling tools like index_bloat (index-level) and bloat_and_vacuum_analysis (comprehensive analysis).

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 implies usage for monitoring table bloat but does not explicitly state when to use this tool versus alternatives like bloat_and_vacuum_analysis or index_bloat. No when-not-to conditions or prerequisites are given.

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