Normalize invoice number

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

With no annotations provided, the description carries the full behavioral burden. It specifies the exact transformation steps: strip punctuation, uppercase, and drop common prefixes, and it demonstrates the canonical collision effect with examples. It does not explicitly state the output type, but the canonical form is strongly implied.

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 sentence that front-loads the purpose, then gives the algorithm and examples. Every element contributes to agent understanding, with no filler or redundancy.

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 tool is simple, has one well-documented parameter, and the description covers purpose, transformation rules, and examples. The only minor omission is an explicit statement of the return value, but the canonical form is clearly implied by the examples.

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 input schema already fully documents the 'number' parameter. The description adds useful examples of acceptable inputs, but it does not significantly extend parameter meaning beyond what the schema provides.

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 ('Normalize'), a specific resource ('invoice number'), and a concrete purpose ('for duplicate detection'). Concrete examples ('INV123', '#INV123', 'NV123') make the behavior unmistakable and distinguish it from unrelated siblings like audit_invoices or evaluate_covenants.

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 description gives clear context for use: normalization for duplicate detection. It does not explicitly name alternatives or state when not to use it, but no sibling tool serves a comparable normalization role, so the context is sufficient.

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