get_column_summary

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

No annotations provided, but the description thoroughly discloses behavior: auto-detects column type and lists all computed statistics for continuous, categorical, binary, temporal, and high_cardinality columns.

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 structured with bullet points for clarity and front-loaded with the main action. However, it is somewhat verbose in listing all statistics, which could be more concise.

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 of column types, the description comprehensively covers return values. It provides usage context relative to load_dataset but lacks details on error cases or edge conditions.

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%, and the description does not explain parameters (file_path, column, table) in detail. The table parameter is not mentioned at all, leaving ambiguity about its role.

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 'Return full summary statistics for a single column' and details the specific statistics for each type, distinguishing it from siblings like 'get_all_summaries' and 'load_dataset'.

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?

Explicitly says to use 'after calling load_dataset to identify columns of interest', providing context for when to use this tool. However, it does not explicitly contrast with 'get_all_summaries' or mention 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.