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

The description goes significantly beyond the annotations by explicitly detailing the system effects: 'application facts corrected; submission touch re-dated when the date moves.' It also adds critical behavioral constraints like 'never a guess' and the mandate to 'Say where the fact came from.'

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?

While the description is dense, it is highly functional. Every sentence contributes unique behavioral context (the 'Effects' and 'never a guess' rule) that cannot be found in the schema or annotations, making the density necessary and justified.

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?

Given the complexity of updating application data, the description is highly complete. It defines what is being fixed, why it is fixed, and what the side effects are, allowing the agent to determine if this is the correct tool for a data correction task.

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?

Since schema coverage is 100%, the description successfully adds semantic value by defining value constraints: the `reason` must contain a source, and `applied_at` can be the specific literal 'unknown.' This prevents misuse of the fields.

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 specific resources being modified (applied_at date or via_contact submission route) and when this tool is necessary (when facts surface). It distinguishes this targeted correction tool from generic update tools by focusing on correcting 'unknown' or 'wrong' values.

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 provides clear criteria for appropriate usage by requiring the input to be a 'sourced fact' and explicitly stating that correction is needed when current data is 'unknown' or incorrect. This narrows the use case effectively, guiding the agent to use this tool only when facts are certain.

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