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

No annotations are provided, so the description carries the full burden. It does hint that the deliverable is a static prompt pack with examples (implying no live computation or data mutation), which is mild useful context, but it never states whether any data is read or written, whether output is deterministic, or what the response contains. For a tool with zero annotation coverage, this is a substantial gap.

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?

Two sentences, but both are marketing filler ('production-ready', 'structured, repeatable workflow you can run today') rather than operational information. The text is short yet nearly every phrase is non-informative, so it fails the 'every sentence earns its place' test.

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?

With no annotations, no output schema, and a vague one-parameter schema, the description needed to explain what the tool returns and how it fits alongside its four anomaly siblings. Instead it leaves the core questions unanswered, so an agent cannot confidently choose or invoke it.

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?

There is a single optional 'query' parameter at 100% schema description coverage, so the schema already documents it. The description adds nothing about how the query scopes the analysis or what happens when it is omitted. Per the high-coverage baseline, a 3 is the appropriate score.

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 essentially restates the name: 'anomaly formula tool system' adds no verb-and-resource specificity beyond the title. It never explains what a 'formula' output actually is (calculation definitions? metric templates? anomaly thresholds?) or how it differs from anomaly-analyze-tool, anomaly-diag-tool, anomaly-board-tool, or anomaly-tpl-tool. The only concrete claim is delivery format ('copy-paste prompt pack with examples'), not purpose.

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?

There is no when-to-use guidance and no routing to alternatives, despite four close siblings sharing the 'anomaly-' prefix. 'For marketing and operations leaders' is an audience label, not a usage condition, and 'a workflow you can run today' gives no trigger for selecting this over the analyze/diag/board/tpl variants.

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