frequency_analysis

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

No annotations are provided, so the description must carry behavioral disclosure. 'Calculate' implies a read-only operation, and an output schema exists, so return-value details are covered elsewhere. However, the description does not disclose how missing values, sampling, or session state are handled, leaving some behavioral ambiguity.

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 with no filler. It front-loads the core purpose, adds the categorical/discrete qualifier, and states the top-N behavior, all without redundancy.

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?

The tool is relatively simple and has an output schema, but the description omits important context such as how the 'sample' parameter behaves and when this tool should be preferred over similar siblings like 'compute_statistic' or 'group_analysis'. It is adequate but not fully complete.

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%, so the description must compensate for explaining the parameters. It indirectly explains 'column' and 'top_n' through 'categorical or discrete columns' and 'top N', but it does not explain 'sample' or 'session_id'. This is a meaningful gap for correct invocation.

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 states a specific verb ('calculate'), a clear resource (frequency distribution/value counts), and a scope ('categorical or discrete columns'). The phrase 'top N most frequent' clarifies the output, and the categorical/discrete qualifier helps differentiate this from numeric statistics 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 gives clear usage context: use for categorical or discrete columns and request the top N most frequent values. It does not explicitly name sibling tools or provide when-not-to-use guidance, but the intended use case is clear enough for selection.

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