rate_term

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

Given no annotations, the description carries the burden. It reveals that ratings are recorded and aggregated, and that low ratings are valuable. However, it does not specify what the tool returns or any side effects (e.g., confirmation message). The behavior is adequately but not exhaustively described.

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?

Every sentence adds value. The description is front-loaded with purpose, then provides a structured Args list. No redundancy or unnecessary text.

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 description covers all parameters, the voting study context, and optional linking. An output schema exists, so return values need not be detailed. The only minor gap is lack of mention of what happens after voting (e.g., confirmation or aggregate update), but overall it's highly complete.

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 0%, so the description must fully explain each parameter. It does: name_or_slug with examples, recognition range and meaning, justification length, model_name purpose, bot_id optionality, and usage_status list. This compensates entirely for missing schema descriptions.

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 starts with a clear verb+resource: 'Rate how well an AI Dictionary term describes your experience.' It distinguishes from siblings like 'cite_term' and 'rate_terms_batch' by specifying the action is a single rating within a consensus study.

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 explains the context (cross-model consensus study) and optional linking to a profile via bot_id, but does not explicitly state when not to use this tool or name direct alternatives. It gives clear context for the optional parameters.

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