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

With no annotations, the description carries the full burden. It adds genuinely non-obvious context: cost ($0.005 per call, USDC on Base), which is valuable. However, it omits whether normalization returns a canonical string, how invalid inputs are reported, and what the return shape is.

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 front-loaded sentence with the action first and the cost detail parenthetically appended. Nothing wasted, nothing buried.

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 tool with no output schema and no annotations, the description still leaves the return value (canonical number vs boolean) and error behavior unclear. The pricing disclosure is helpful but doesn't complete the behavioral picture.

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% and the single 'value' parameter is already documented as 'mobile in any common format', so the schema does the heavy lifting. The description's 'normalize' hints at an output convention but adds no syntax or format detail beyond the schema.

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 ('validate and normalize') and a specific resource ('Iranian mobile number'). It clearly distinguishes itself from the sibling fa_validateLandline and the other fa_validate* tools by naming the exact data type.

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 purpose implies when to use it (validating a mobile number), but the description offers no explicit conditions, no mention of the natural alternative fa_validateLandline, and no guidance on when normalization applies versus plain validation. Usage is only inferable.

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