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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=false; the description adds meaningful context beyond that by calling the tool 'Pure computation' and listing the returned fields (recommendation, alternatives, reasons, cautions), which tells the agent the output is advisory and justification-bearing rather than prescriptive. It also discloses the reasoning provenance (Few, Tufte) with a disclaimer that rules are the author's own, which usefully signals the recommendations are heuristic, not deterministic.

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?

A single dense paragraph that front-loads the decision, then parameters, then return values, then provenance — a sensible order with no filler sentences. The long inline parameter list is efficient but slightly unpaced; a mild structure improvement (bullets or grouping) would aid scanning.

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?

There is no output schema, but the description names all four return fields, so the agent knows what to expect back. Combined with full parameter glossing and the safety profile from annotations, nothing an agent needs to select or invoke this tool correctly is missing.

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 carries the full burden, and it does: every one of the nine parameters is glossed in prose, with non-obvious ones clarified (cols as 'numeric columns or series', groups as 'how many comparable series or panels could be drawn'), and the enum-bearing parameters labelled with their decision role (purpose for lookup/compare/pattern/monitor, space for output footprint). This is materially more than the schema's bare enum lists.

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?

Names a specific decision task (table vs small multiples vs sparkline vs chart) with a clear verb and output artifact, so an agent can tell exactly what problem this resolves. The scope is narrow and stated up front, distinguishing it from generic chart-linting or colour tools in the sibling list.

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 opening sentence plus the enumerated inputs imply the situation in which to call it (you have a dataset shape and a display purpose and need a form recommendation). It does not, however, explicitly rule out or point to near neighbours such as chart_chooser or chart_lint, so an agent still has to infer that this tool decides form while those evaluate an existing chart.

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