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

With no annotations, the description carries the behavioral burden. It discloses that grouping commas and currency are handled and that non-numeric rows are excluded and counted, which is valuable. However, it doesn't mention whether the operation is read-only, error handling, or performance, but for a stats tool these are less critical. It adds context beyond the schema.

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?

One concise sentence that packs all essential information: the operation, the output statistics, and data handling behaviors. No filler, perfectly front-loaded. It earns its place.

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?

For a simple one-parameter tool with no output schema, the description is fairly complete. It specifies what statistics are returned and how data quirks are handled. It doesn't cover edge cases like missing columns or empty results, but those are minor. Overall, an agent can correctly invoke it without further clarification.

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 schema has one parameter 'column' with no description, and schema coverage is 0%. The description mentions 'numeric column of the Mustvo dataset', which implies the parameter is a column name and that it must be numeric. However, it doesn't provide examples, format expectations, or clarify case sensitivity or exact naming. It provides some meaning but not fully compensates for the lack of schema descriptions.

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 verb (compute) and resource (numeric column of the Mustvo dataset) and lists the exact statistics returned. It distinguishes from siblings like dataset_row or dataset_search by focusing on aggregation, making it unmistakable what this tool does.

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?

While not explicitly stating when to use this vs alternatives, the specific purpose (summary statistics for a numeric column) strongly implies its use case. It doesn't mention exclusions or alternatives, but the sibling names (columns, compare, row, search, top) are distinct enough that confusion is unlikely. It's clear but could have explicitly stated 'use this when you need column statistics'.

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