table_stats

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

No annotations are provided, so the description carries full burden. It does not disclose whether the operation is read-only, any potential performance impact, required permissions, or return format. The description only states what statistics are shown.

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 front-loaded with the tool's purpose. Every word is meaningful with no redundant or ambiguous phrasing.

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 3 optional parameters, no output schema, and no annotations, the description is insufficient. It does not explain behavior when no table is specified, how to interpret return values, or any constraints like database/profile filtering.

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 coverage is 100% with descriptions for each of the 3 parameters, so baseline is 3. The tool description adds context about the output (row counts, sizes, time) but does not enhance understanding of parameters beyond what the schema already provides.

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 a specific verb 'Show' and clearly identifies the resource as 'table statistics', listing concrete metrics like row counts, data/index sizes, and update time. This clearly distinguishes it from sibling tools such as list_tables or describe_table.

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 lacks any guidance on when to use this tool versus alternatives, no mention of prerequisites, exclusions, or context. It simply states what the tool does without providing decision criteria.

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