extract_tokens_from_json

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

No annotations are provided, so the description carries the full burden. It states the tool parses JSON into universal tokens but does not disclose behavioral traits such as error handling, performance characteristics, or what 'universal tokens' entail. This leaves significant gaps for a tool with no annotation coverage.

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, efficient sentence that directly states the tool's purpose without any wasted words. It is appropriately sized and front-loaded, making it easy to understand at a glance.

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 (parsing JSON into tokens), lack of annotations, and no output schema, the description is minimal. It states the purpose but does not cover behavioral aspects or output details, making it adequate but with clear gaps for informed tool selection.

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%, so the schema already documents the single parameter 'json' as a W3C DTCG format JSON string. The description adds no additional meaning beyond what the schema provides, such as format details or examples, resulting in the baseline score of 3.

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 specific action ('Parse'), the input format ('W3C DTCG format JSON'), and the output ('universal tokens'), distinguishing it from siblings like extract_tokens_from_css or extract_tokens_from_figma_variables by specifying the input format.

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 implies usage when dealing with W3C DTCG format JSON, but does not explicitly state when to use this tool versus alternatives like extract_tokens_from_css or validate_contrast. It provides clear context but lacks explicit exclusions or named alternatives.

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