autovacuum_advisor

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

With no annotations, the description's use of 'recommend' hints that the tool does not apply changes, but it does not explicitly state read-only behavior, input requirements, or output format. This leaves uncertainty about whether it modifies settings or just reports.

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 a single concise sentence that front-loads the primary action. Every word is purposeful, with no redundancy.

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 tool lacks an output schema and annotations, yet the description does not explain what the analysis returns, whether it requires a live database, or what 'tuning settings' means. This is underspecified for an agent to invoke confidently.

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 description does not mention the table, schema, or database_name parameters, all of which are optional. With 0% schema coverage, the description fails to clarify how these parameters scope the analysis, so an agent gets no added meaning beyond parameter names.

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 uses the verb 'analyze' with resources 'autovacuum effectiveness' and 'tuning settings', clearly identifying the tool's function. It distinguishes itself from siblings like query or list_objects by focusing on autovacuum tuning.

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 is provided about when to use this tool versus alternatives. The description implies usage for autovacuum tuning scenarios, but does not mention prerequisites or cases where other diagnostic tools would be more appropriate.

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