get_autovacuum_status

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

With no annotations provided, the description carries full burden and does well by specifying what the tool does (analysis only) and what it doesn't do (no changes, no manual execution). It discloses behavioral constraints like 'analyzes all user schemas if omitted' and default parameter behavior. However, it doesn't mention potential performance impact of the analysis or any rate limits.

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 well-structured with clear sections ([Tool Purpose], [Exact Functionality], etc.), front-loading the purpose, and every sentence earns its place by providing specific guidance or information. No redundant or vague statements are present.

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?

Given the tool's complexity (analysis of autovacuum status with 4 parameters), no annotations, and the presence of an output schema (which handles return values), the description is complete. It covers purpose, functionality, usage guidelines, prohibitions, parameter semantics, and return overview, leaving no significant gaps for the agent to understand and invoke the tool correctly.

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 compensates well by explaining all 4 parameters in the Args section, including default behaviors (e.g., 'uses default database from POSTGRES_DB env var if omitted'), filtering logic ('analyzes all user schemas if omitted'), and format details ('SQL LIKE pattern'). It adds meaningful context beyond the bare schema, though it could provide more examples for the table_pattern parameter.

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 explicitly states the tool's purpose as 'Analyze autovacuum configuration and current maintenance status for tables' with specific functionality details like analyzing trigger conditions, calculating dead tuple ratios, and identifying tables needing attention. It clearly distinguishes from sibling tools like 'get_autovacuum_activity' (which likely shows active operations) and 'get_vacuum_effectiveness_analysis' (which likely evaluates past vacuum performance).

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 provides explicit 'Required Use Cases' (e.g., when user requests autovacuum status, planning optimization, troubleshooting) and 'Strictly Prohibited Use Cases' (e.g., configuration changes, manual VACUUM execution, process restart). This gives clear guidance on when to use this tool versus alternatives like configuration modification tools or manual vacuum execution tools.

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