get_statistics

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

With no annotations provided, the description carries the full burden. It discloses that the tool is optimized for AI workflows and returns comprehensive statistical analysis, but does not cover behavioral aspects like performance, error handling, or data size limitations. It adds some context (e.g., 'percentiles always included') but lacks details on permissions, rate limits, or mutation effects. The transparency is adequate but has gaps.

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 for returns, metrics, examples, and AI integration, making it easy to scan. However, it includes redundant elements (e.g., repeating 'Analyze all numeric columns' in examples) and could be more front-loaded by moving key usage details earlier. Most sentences earn their place, but some trimming would improve conciseness without losing clarity.

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, the description is reasonably complete. It covers purpose, metrics, examples, and integration, but lacks details on behavioral traits and explicit sibling differentiation. The output schema likely handles return values, so the description does not need to explain them. It is mostly sufficient but could be more comprehensive for a tool with no annotations.

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 'columns' parameter. The description adds minimal value beyond the schema, mentioning 'all or specified numerical columns' and providing examples, but does not explain parameter semantics in depth (e.g., column name formats, handling of non-numeric columns). With high schema coverage, the baseline score of 3 is appropriate as the description does not significantly enhance parameter understanding.

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 computes descriptive statistics for numerical columns, specifying the exact metrics (count, mean, standard deviation, min/max, percentiles). It distinguishes from siblings like 'get_column_statistics' by emphasizing 'comprehensive statistical summary' and 'all or specified numerical columns', though the distinction could be more explicit. The purpose is specific but not fully differentiated from similar tools.

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 implies usage for data understanding, quality assessment, and AI workflows, and provides examples for analyzing all or specific columns. However, it lacks explicit guidance on when to use this tool versus alternatives like 'get_column_statistics' or 'profile_data', and does not mention prerequisites or exclusions. The guidelines are helpful but incomplete for sibling differentiation.

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