get_column_statistics

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 like returning 'detailed statistical analysis' with specific components (data type, statistics, non-null count, distribution). However, it lacks details on error handling, performance characteristics, or limitations (e.g., column existence validation, data size constraints).

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 sections (Returns, Column Analysis, Examples, AI Workflow Integration) and uses bullet points for clarity. It is appropriately sized but could be more concise by integrating some repetitive elements (e.g., 'Detailed statistical analysis' appears twice).

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 (statistical analysis), no annotations, and the presence of an output schema (implied by 'Has output schema: true'), the description is complete. It covers purpose, usage, parameter context, and behavioral aspects adequately without needing to explain return values due to the output schema.

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 input schema has 100% description coverage, clearly documenting the 'column' parameter. The description adds value by providing examples of usage ('price', 'category') and context in the 'Column Analysis' section, which elaborates on what the analysis entails beyond the schema's basic parameter definition.

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 clearly states the tool's purpose with specific verbs ('Get detailed statistical analysis', 'Provides focused statistical analysis') and identifies the resource ('for a single column'). It distinguishes from siblings like 'get_statistics' (general) and 'get_data_summary' (overall) by emphasizing single-column focus and detailed statistical analysis.

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 'AI Workflow Integration' section provides clear context for when to use this tool (e.g., 'Deep dive analysis for specific columns', 'Data quality assessment for individual features'). However, it does not explicitly state when NOT to use it or name specific alternatives among siblings, such as 'get_statistics' for broader analysis.

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