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mimic_discover_ds

Idempotent

Extract and normalize a design system from a Figma library file to generate a structured knowledge artifact with counts of components, styles, and variables.

Instructions

Extract and normalize a design system from a Figma library file. Call this on first run or when the DS has been updated. Queries the Figma REST API for components, component sets, styles, and variables. Normalizes the extraction into a structured knowledge artifact at internal/ds-knowledge/ds-knowledge-normalized.json. Returns a summary of what was found (counts of components, styles, variables).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fileKeyYesFigma file key for the DS library file.
Behavior4/5

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

Beyond annotations (idempotent, not destructive), description adds that it queries the Figma REST API, normalizes into a specific file path, and returns counts. This gives a solid understanding of external side effects and output format. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, front-loaded with purpose, then usage, then behavior. No filler or repetition. Could be slightly more structured but very efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers operation (extraction, normalization), output (summary counts), and storage location. Given the complexity and absence of an output schema, the description is fairly complete. Lacks mention of error cases or performance, but acceptable.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, and the schema already describes the sole parameter (fileKey). The description does not add additional parameter-level meaning, meeting the baseline of 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states 'Extract and normalize a design system from a Figma library file' with a specific verb and resource, and indicates when to call. However, it does not explicitly distinguish from sibling tools like figma_discover_library_styles or figma_discover_library_variables, which might cause confusion about when to use this comprehensive tool versus the specific ones.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says 'Call this on first run or when the DS has been updated,' providing clear timing guidance. However, it does not mention when not to use it or suggest alternatives for targeted discovery, though the sibling tools imply those alternatives.

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

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