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

With no annotations, the description carries the full burden. It discloses the validation logic (length + mod-10 checksum), the exact return shape ({valid, type, normalized}), and rejection behavior with a concrete example. Minor ambiguity remains around what 'rejects' means (false vs error) and normalization semantics, but this is strong coverage for a simple tool.

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, zero filler, front-loaded with the core function, then return shape and rejection example. Every clause earns its place.

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 single-parameter validation tool with no output schema, the description explains the return value and validation rule. It omits details like the meaning of 'normalized' and behavior on non-digit input, but the tool is simple enough that these are minor gaps.

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 coverage is 100%: the 'code' parameter is already described as 'Candidate product code, digits only'. The description adds no extra parameter semantics beyond echoing the schema, so the baseline of 3 applies.

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?

States a specific verb ('Check a product code') and resource ('GTIN rules') with precise criteria: length + mod-10 checksum. This clearly separates it from siblings like decode_barcode, list_formats, and render_barcode, which serve different purposes.

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?

Usage context is implied by the purpose: use when you need to validate a GTIN/EAN/UPC code rather than decode or render it. However, it never explicitly states when to use this tool instead of alternatives, and no exclusions or conditions are given.

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