get_token

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

Although annotations already declare readOnlyHint=true, the description adds meaningful behavioral detail: 'Read-only, no side effects,' explains the return value, and discloses the error condition when the token is not found. It also explains alias normalization for categories, which goes beyond the annotation.

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 composed of three well-structured sentences, with the primary action and read-only guarantee front-loaded, followed by return/error behavior, then category semantics and sibling-tool guidance. Each sentence earns its place, though the category-alias list makes it slightly denser than strictly necessary.

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 simple two-parameter look-up tool, the description is complete: it covers input semantics, return behavior, error outcome, category normalization, and alternative tool usage. No output schema is present, but the description explicitly lists what the return contains and the error case, so the agent can predict behavior.

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 input schema has zero description coverage, so the description carries the full burden of explaining parameters. It explains that 'name' is the token name to search for and 'category' narrows the search, listing all valid categories and alias normalization behavior. This adds substantial meaning beyond the raw 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 clearly states the tool's purpose: 'Look up a specific design token by name' and specifies the exact resource (design tokens), the return value, and the error behavior. It is distinguished from sibling tools like get_design_context by explicitly targeting single-token lookup vs broad overview.

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 clear when-to-use guidance: 'Use this when you know the token name.' It also provides an explicit alternative for a different use case: 'For a broad overview of all tokens, use get_design_context with category 'tokens' instead.' This is model guidance for tool selection.

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