get_index_io_stats

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 explaining what the tool does (analyzes I/O statistics, identifies performance issues, helps optimization) and what it doesn't do (no optimization actions, configuration changes, or statistics reset). It could improve by mentioning if this is a read-only operation or has any side effects, but provides substantial behavioral context.

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.) and front-loaded with purpose. While somewhat verbose, every sentence adds value (use cases, prohibitions, parameter explanations) and there's no redundant information.

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 analytical nature, 2 parameters, no annotations, but with an output schema (confirmed by context signals), the description is complete: it explains purpose, functionality, use cases, prohibitions, parameters, and return values. The output schema handles return format details, so the description appropriately focuses on context and usage.

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 description coverage is 0%, but the description compensates by explaining both parameters in the Args section: 'database_name: Database name to analyze (uses default database if omitted)' and 'schema_name: Schema name to filter (default: public)'. This adds meaning beyond the bare schema, though it doesn't provide format examples or constraints.

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 I/O performance statistics for indexes (disk reads vs buffer cache hits)' with specific verbs ('analyze', 'show', 'identify', 'provide', 'help optimize') and clearly distinguishes it from siblings like get_table_io_stats (index vs table focus) and get_index_usage_stats (I/O performance vs usage patterns).

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 guidance with '[Required Use Cases]' listing four specific scenarios and '[Strictly Prohibited Use Cases]' listing three clear exclusions, giving comprehensive when-to-use and when-not-to-use instructions that help differentiate from potential alternatives.

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