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

Annotations already declare the tool as read-only, idempotent, non-destructive, and closed-world. The description adds useful context about what the tool returns (formula, read values, ultimate inputs) and that the explanation reflects Valem's engine for the given inputs, which helps since there is no output 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?

Three tight sentences with the core purpose and trigger front-loaded, followed by parameter sourcing. No wasted language or repetition.

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?

The description compensates for the absence of an output schema by explaining what the explanation contains, and it covers parameter sourcing and usage context. Minor gaps like the spec_unknown fallback are covered in the schema rather than the description, so it is not quite perfect.

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 100%, so the baseline is 3. The description adds meaningful provenance guidance for specHash and inputs by pointing the agent to the latest render_model result or card update, going beyond the schema's own 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 states a specific verb and resource: it explains how a value in a Valem card was computed, including the formula, values read, and input provenance. This is clearly distinguishable from generic sibling tools like explain or eval_expression.

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?

It gives a clear trigger ('when the user asks why a number is what it is') and tells the agent where to obtain the key parameters ('from the latest render_model result or card update'). It does not explicitly name alternatives or when-not-to-use cases, so it falls short of a 5.

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