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conductor-figma

conductor-figma

by Dragoon0x
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
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suggest_improvements

Analyze a selected Figma node to receive design improvement suggestions based on built-in design rules, type scales, colors, and accessibility checks.

Instructions

AI-powered design improvement suggestions based on the design intelligence engine.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "nodeId": "<nodeId>"
    },
    "name": "suggest_improvements"
  }
}

Input Schema

NameRequiredDescriptionDefault
nodeIdYesNode to analyze

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv3.3.3

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It says the tool is AI-powered but does not disclose whether it mutates, what it returns, latency, permissions, or failure modes. The phrase 'based on the design intelligence engine' gives minimal context but not enough for an agent to predict behavior.

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?

The description is one short sentence and is easily scanned. It is concise, though 'AI-powered' and 'based on the design intelligence engine' are somewhat redundant filler rather than high-value operational content.

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

Completeness2/5

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

The description is thin for a tool with no output schema and no annotations. It does not explain what the suggestions look like, whether they are returned as text or structured data, or how the tool relates to the many similar audit/suggestion/check tools. An agent has too little context to call it confidently.

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%, so the schema already documents the only parameter,nodeId, as 'Node to analyze'. The description adds no extra parameter-level meaning, so the baseline 3is appropriate.

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?

The description clearly identifies the tool's purpose: generating AI-powered design improvement suggestions. However, it does not explicitly say the suggestions target a node or how they differ from more specific sibling tools like suggest_color_palette or suggest_type_scale, so it stops short of full differentiation.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives such as detect_inconsistencies, lint_design, or compare_to_system. An agent must infer usage from the schema and sibling names, which is a significant gap.

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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