bloat_and_vacuum_analysis

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

Starts with '[READ]' to indicate read-only behavior, especially important since no annotations are provided. Explains two modes and that each recommendation cites numbers. Does not mention potential performance implications of live mode, but overall transparent.

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 consists of two short paragraphs, front-loaded with the purpose and [READ] tag. Every sentence adds value with no fluff.

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?

Covers purpose, usage modes, parameters, and a hint about output (numbers). However, it does not explicitly describe the output format (e.g., list of tables with scores) or the exact ranking criteria, which 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?

With 0% schema description coverage, the description fully explains each parameter: 'tables' (injection vs. live), 'limit' (default 50, only when not injected), and 'target' (config target). Adds meaning beyond type and name.

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 'Rank' and the resource 'tables needing vacuum', with the criteria 'dead-tuple ratio + autovacuum recency'. It distinguishes from siblings by mentioning the 'tables' parameter can come from sibling 'table_bloat', indicating a complementary relationship.

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?

Describes two usage modes: offline analysis with injected tables (from table_bloat) and live querying without the 'tables' parameter. However, it does not explicitly exclude scenarios or compare to other analysis tools like autovacuum_status or run_vacuum.

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