frequency_count

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

With no annotations, the description must disclose behavioral traits. It mentions the output structure (JSON list with center, end, count, cumulative), which is helpful, but does not indicate side effects, permissions needed, or whether the tool modifies data. This is minimally adequate for a read-only tool.

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 short and front-loaded with purpose, but the parameter list covers only half the params, making it inadequate. Every sentence is useful but incomplete.

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 has 6 required parameters and no annotations, the description is insufficient. It fails to explain three essential parameters (data_book, data_sheet, col) and does not provide context about selecting data sources, which is crucial for correct invocation.

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 0%, so the description must explain all parameters. It only explains bin_min, bin_max, and bin_size, ignoring data_book, data_sheet, and col. These three are critical for specifying the data source and column, and their omission leaves the agent guessing.

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 explicitly states it produces 'Histogram-style frequency counts for one column,' which clearly defines the tool's purpose and distinguishes it from siblings that perform other statistical operations like curve_fit or column_statistics.

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?

No guidance is provided on when to use this tool versus alternatives such as column_statistics or compare_means. The description lacks context about prerequisites, data preparation, or scenarios where this tool is appropriate.

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