suggest_category

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

No annotations are provided, so the description must convey behavioral expectations. It states it is a 'Get' operation ('auto-suggestion'), which implies a read-only, non-destructive action. However, it does not disclose the response format (e.g., whether it returns a single suggestion or multiple, confidence scores, or behavior for unknown payees). The description is adequate but leaves gaps in behavioral transparency.

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 exceptionally concise, consisting of two short sentences (18 words). It is front-loaded with the core action ('Get ZenMoney's auto-suggestion') followed by the input and use case. Every word adds value, and there is no redundancy or filler.

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: one string parameter, no output schema. The description adequately explains what it does, why it is useful, and identifies the key input. It does not mention error behavior or return format, but given the simplicity, it is mostly complete. The absence of annotations and output schema means the description could offer slightly more, but it still provides sufficient context for a simple read-only suggestion utility.

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% since the only parameter 'payee' is described as 'The payee/merchant name from the receipt.' The tool description adds minimal extra meaning by mentioning 'payee name' and 'receipts,' but this largely repeats the schema. No additional parameter details are provided beyond the schema, so the baseline score of 3 is appropriate.

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 states the tool's function: 'Get ZenMoney's auto-suggestion for category and merchant based on a payee name.' The verb 'Get' and the specific resource ('auto-suggestion for category and merchant') make the purpose unambiguous. It also distinguishes from sibling tools like list_categories and create_category by focusing on the suggestion feature rather than direct category management.

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 phrase 'Useful for categorizing receipts' provides a clear context for when to use this tool. However, it does not explicitly mention alternatives or cases where this tool should not be used, despite having many sibling tools for category and transaction management. The use case alone gives sufficient guidance for an agent to decide when to invoke it.

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