suggest_tokens

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

With no annotations provided, the description carries the full burden. It explains the tool reads context, scores tokens, and returns top N with reasoning. It implies a read-only operation, though it lacks explicit mention of side effects or safety, which is acceptable given the nature.

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 two sentences, each earned: first states the core function, second provides usage guidance. It is front-loaded with the main action and contains zero redundant or vague language.

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?

Given the tool's complexity (5 parameters, no output schema), the description is incomplete. It omits details about parameter usage and return value structure. The tool likely needs parameter context for correct invocation, which is missing.

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% meaning no parameter descriptions. The description does not explain any of the five parameters (max, url, nodeId, fileKey, propertyKey). The tool requires understanding these fields, and the description provides no help, failing to compensate for the lacking schema.

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 uses a specific verb ('Suggest'), identifies the resource ('tokens from the loaded catalog'), and specifies the context ('that fit a Figma node, ranked'). It clearly distinguishes from sibling tools like 'debug_resolve_token' by emphasizing ranking and reasoning.

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 explicitly states when to use this tool: when naming convention isn't obvious or when user says 'tokenize this'. This provides clear contextual guidance, though it does not discuss when not to use it or mention alternatives.

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