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

With no annotations, the description carries the burden, and it does well by disclosing preprocessing behavior ('grouping commas and currency are handled') and data cleaning ('non-numeric rows are excluded and counted'). This adds genuine behavioral context beyond the schema. It doesn't mention null handling or output shape, so it stops short of a 5.

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 efficient sentence: it front-loads the operations, then the dataset, then the edge-case handling. Every element earns its place with no filler or redundant phrasing.

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 one-parameter read-only stats tool, the description supplies the necessary context: what the tool operates on, what it returns conceptually, and how it treats non-numeric data. The phrase 'excluded and counted' is slightly ambiguous, but the overall picture is sufficient for a simple tool.

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. It does clarify that the single parameter refers to a numeric column of the Markupbird dataset and that stats apply to that column. However, it omits details like accepted column naming conventions, case sensitivity, or error conditions if the column doesn't exist, so it's adequate but not complete.

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 six named summary statistics (count, min, max, mean, median, sum) on a numeric column of a specific dataset, which is a specific verb+resource operation. It doesn't explicitly differentiate from siblings like dataset_top or dataset_search, but the operation is distinctive enough to identify.

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 tool's use case is implied: call it when you need summary statistics for a numeric column. However, it gives no explicit guidance about when not to use it or how it compares to alternatives like dataset_top or dataset_search, leaving selection to inference.

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