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Validate design fidelity

designfit_validate

Validate a rendered front-end against its Figma design to get a fidelity score and a machine-actionable fix-list of token, geometry, and presence violations.

Instructions

Validate a rendered front-end against its Figma design. Tag each built element with data-designfit-id="", pass the design spec (geometry + tokens, from designfit_extract or relayed from Figma's MCP), the running URL, and the component map. Returns a fidelity score and a machine-actionable fix-list of token, geometry, and presence violations.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYes
designYes
viewportYes
tolerancesNo
componentMapYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does disclose two non-schema behaviors: the page must be instrumented with data-designfit-id before validation, and the tool returns a fidelity score plus a machine-actionable fix-list of token/geometry/presence violations. It omits auth/permission needs and any rate or rendering limitations, but the operational model is largely conveyed.

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?

Three sentences, front-loaded with purpose, then the setup/instrumentation requirement, then the return values. Dense and free of filler; every clause adds information.

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?

There is no output schema, so the description correctly explains what comes back (fidelity score + fix-list). For a 5-param tool with nested objects and no annotations it is largely complete, though it leaves viewport and tolerance controls unexplained.

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 description coverage is 0%, so the description must compensate. It meaningfully clarifies that design is a geometry+tokens spec and that componentMap links selectors to figmaNodeId (via the data-designfit-id tagging), but it never mentions viewport or tolerances, leaving two of five parameters semantically undocumented.

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?

States a specific verb and resource: 'Validate a rendered front-end against its Figma design.' It also distinguishes itself from its sibling by naming designfit_extract as the source of the design spec, so an agent can route between the two without opening either schema.

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?

The description lays out the preconditions for a successful call (tag elements with data-designfit-id, supply the spec, the running URL, and the component map) and points to designfit_extract / Figma's MCP as spec sources. It conveys the workflow clearly but does not state a when-not or an explicit alternative-validation path.

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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