get_table_io_stats

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

No annotations are provided, so the description carries the full burden. It discloses behavioral traits such as the tool's analytical nature (not for optimization actions), the types of statistics shown (heap, index, TOAST), and performance analysis capabilities. However, it doesn't mention potential limitations like data freshness, permissions required, or rate limits, which would be useful for a tool with no annotation coverage.

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.), making it easy to parse. It's appropriately sized with no redundant information. However, some sections like '[Exact Functionality]' could be more concise, as bullet points like 'Calculate buffer hit ratios for performance analysis' and 'Identify tables with poor buffer cache performance' overlap slightly in intent.

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 complexity (analytical tool with no annotations), the description is complete. It covers purpose, functionality, usage guidelines, parameters, and return values. With an output schema present, the description doesn't need to detail return values, and it adequately explains what the tool does and when to use it, making it sufficient for an AI agent to select 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?

Schema description coverage is 0%, but the description compensates by explaining the 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, clarifying defaults and usage. However, it doesn't provide additional context like valid values 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 in the '[Tool Purpose]' section: 'Analyze I/O performance statistics for tables (disk reads vs buffer cache hits)'. It provides a specific verb ('Analyze') and resource ('I/O performance statistics for tables'), and distinguishes from siblings like 'get_index_io_stats' by focusing on tables rather than indexes.

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 includes '[Required Use Cases]' with specific scenarios (e.g., 'When user requests "table I/O stats"', 'When analyzing table-level I/O performance') and '[Strictly Prohibited Use Cases]' with clear exclusions (e.g., 'Requests for I/O optimization actions', 'Requests for buffer cache configuration changes'). This provides explicit guidance on when to use this tool versus alternatives or when not to use it.

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