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sso-ss

figma-unified-mcp

by sso-ss

figma_match_components

Match Figma components to codebase components via fuzzy name matching, producing confidence scores for review.

Instructions

Match Figma components to codebase components using fuzzy name matching. Requires plugin connection for Figma scanning, and a codebase path for code scanning. Returns a mapping with confidence scores. Review before publishing.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fileKeyNoFigma file key. Optional if plugin is connected (scans current page).
frameworksNo
codebasePathYesPath to codebase root directory. Supports ~.
minConfidenceNoMinimum match confidence to include (0-1). Default: 0.5
Behavior3/5

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

With no annotations provided, the description carries the transparency burden. It discloses dependencies (plugin connection, codebase path), the fuzzy matching algorithm, and a warning to review results. However, it does not state whether the operation is read-only, describe any side effects, or outline error conditions. This gives some insight but leaves gaps.

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?

The description is concise: two sentences that front-load the purpose, state prerequisites, and summarize the output and a safety caveat. Every sentence adds meaningful information with no wasted words.

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 (4 parameters, no output schema), the description covers the core aspects: what it does, required dependencies, and the main output (mapping with confidence scores). Minor gaps remain, such as not specifying the structure of the mapping or behavior when no matches are found, but it is largely complete for an AI agent to understand the tool's role.

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?

The schema already provides descriptions for 3 of 4 parameters (fileKey, codebasePath, minConfidence), giving 75% coverage. The description adds no parameter-specific details beyond what the schema provides, such as how frameworks or minConfidence influence the matching. It mentions codebasePath and plugin connection, but these are already in the schema. Baseline 3 is appropriate.

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 the tool's function: 'Match Figma components to codebase components using fuzzy name matching.' This is a specific verb (match), resource (Figma components to codebase components), and method (fuzzy name matching). It distinguishes from sibling tools like figma_scan_codebase or figma_get_components by focusing on the matching capability.

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

Usage Guidelines3/5

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

The description provides prerequisites: 'Requires plugin connection for Figma scanning, and a codebase path for code scanning.' It also advises 'Review before publishing.' However, it does not explicitly state when to use this tool over alternatives or mention any exclusions, so usage guidance is only implied rather than explicit.

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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