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hoangpm96

Reqwise Figma MCP

by hoangpm96

figma_read

Read Figma canvas elements and design data. Choose from operations like get selection, nodes, styles, variables, components, or run a layout audit to verify rendered bounds.

Instructions

Read the Figma canvas with a token-frugal response. Choose an operation and pass its params. layout_audit is the structured verify tool (declared vs rendered bounds, overflow, clipping, truncation). read_selection deep-reads the current selection in one call — the entry point of the selection-first edit-in-place lifecycle (read_selection → figma_write modify → layout_audit).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
opYesThe read operation to run.
paramsNoOperation parameters (e.g. { nodeId }, { nodeIds }, { detail: 'sparse'|'compact'|'full' }, read_selection: { detail?, depth? }).
Behavior3/5

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

With no annotations, the description is the sole source of behavioral info. It mentions token-frugal responses and describes the purpose of layout_audit and read_selection. However, it lacks details on failure modes, authentication needs, or rate limits, which are important for a read tool with many operations.

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

Conciseness5/5

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

Four sentences with no wasted words. The first sentence front-loads the core purpose, followed by actionable guidance. Each sentence earns its place.

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?

Given the tool's complexity (17 operations, nested params, no output schema), the description covers the main concept and key operations. It could be improved by mentioning typical return values or error handling, but it is reasonably complete for an experienced user.

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

Parameters5/5

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

The description adds significant meaning beyond the schema: it explains the 'op' parameter structure and gives concrete examples for 'params' (e.g., { nodeId }, { detail }). It also clarifies the role of specific operations like layout_audit and read_selection, which are not fully defined in the schema.

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

Purpose5/5

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

The description clearly states 'Read the Figma canvas', specifying the verb and resource. It also distinguishes from sibling figma_write by implying a read-only scope. The variety of operations is listed, making the purpose unambiguous.

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?

It provides context on when to use layout_audit vs read_selection, and mentions the token-frugal nature for efficiency. However, it does not explicitly exclude alternative tools like figma_docs or figma_rules, or provide when-not-to-use guidance.

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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