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

Annotations already cover the safety profile (readOnly, idempotent, closed-world), but the description adds behavior the annotations do not: it returns one entry per AI with the AI's human name and surfaces format errors instead of silently failing, which matters because there is no output schema. It stops short of saying what a malformed string yields (empty list vs. error field), so it is not a full 5.

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, front-loaded with the action and resource, with the output shape stated last. No filler and nothing to trim.

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, non-destructive parsing tool with no output schema, the description conveys enough: accepted formats, the unit of output, and that errors are reported as data. Only the precise return shape for invalid input is left ambiguous.

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% and the single parameter's description already gives example forms including FNC1 and Digital Link URLs, so the schema carries most of the burden. The description's format list reinforces accepted input types but adds no syntax or encoding detail beyond the schema, matching the baseline 3.

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 (split) and resource (GS1 data), enumerates the accepted formats (GS1-128, GS1 DataMatrix, Digital Link), and says what the output contains (Application Identifiers, AI names, format errors). This is clearly distinct from validate_gtins or identify_code, which check or classify rather than decompose a GS1 string.

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 is only implied: an agent can infer the tool applies to raw GS1 strings needing decomposition, but the description gives no explicit when-to-use, when-not-to-use, or pointer to an alternative sibling such as validate_gtins for validation-only needs.

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