Resolve scanned cards

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

With no annotations provided, the description carries the full burden and does so exceptionally. It discloses key behaviors: quantity optionality, set inference from card numbers, Japanese set code mapping with numbering differences, bare-name resolution preferring newest standard-legal printings with alternatives, and return of unresolved lines with reasons. This is rich, candid behavioral disclosure.

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?

The description is a single dense paragraph that is front-loaded with the purpose, then provides concrete examples and output details. Every sentence contributes value; 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?

For a one-parameter tool with no output schema and no annotations, the description is remarkably complete. It covers input variations, resolution strategies, output format, and downstream integration, leaving no significant gaps for an agent to infer incorrectly.

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?

The schema already covers the 'lines' parameter with a description and example (100% coverage). The description adds substantial extra meaning by illustrating acceptable messy formats, edge cases like Japanese codes, and output expectations, thereby going beyond the schema's baseline.

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 specifies the tool's function: resolving messy raw card identifications from photos into verified printings. It provides concrete input examples and distinguishes itself from sibling tools like search_cards (searching) and collection_add (adding) by focusing on resolution and normalization.

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 strong context on when to use this tool—when lines are messy, include quantities, Japanese codes, or bare names—and explains the output's downstream use with collection_add, check_deck, or build_decks. However, it does not explicitly name alternatives or state when not to use it, falling just short of the highest bar.

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