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compose

Classify natural language design tasks, build multi-step plans, and execute them automatically. Specify components and outputs to generate specs, code, or audits.

Instructions

Run the agent orchestrator with a natural language design intent — classifies the task, builds a multi-step plan, and executes it.

Prerequisites: No Figma connection required for spec/code tasks. Figma-touching tasks (design generation, audits) require the bridge to be running. The orchestrator automatically dispatches to registered agent workers when available, or falls back to internal execution.

Returns on success: Orchestrator result object with shape { success: boolean, plan: { steps: [] }, results: [], summary: string, errors?: [] }. Each step includes the agent role that handled it and its output.

Error behavior: Returns success=false with an errors array if planning fails or execution throws. Individual step failures are captured per-step and do not abort the entire plan.

Intent examples:

  • "create a dashboard page with KPI cards, a chart, and a data table" — generates specs and code

  • "audit button variants for WCAG contrast and touch target compliance" — runs accessibility checks

  • "generate a login page with email/password form and OAuth buttons" — spec + codegen

  • "pull design system, then generate all missing component specs" — chained multi-step pipeline

  • "create a molecule spec for a search bar composing Input and Button atoms" — atomic design authoring

Be specific — vague intents like "make something nice" produce generic plans. Include component names, atomic levels, and target pages when relevant.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
intentYesNatural language design task. Be specific about what to create, modify, or check. Include atomic level if relevant (atom/molecule/organism/template/page), component names, and target output (spec, code, audit). Examples: 'create a KPI card atom with value, label, and trend props', 'audit all organism specs for WCAG 2.2 compliance', 'generate the LoginPage template from the AuthForm organism spec'.
dryRunNoIf true, returns the execution plan without running any steps. Use to inspect what the orchestrator intends to do before committing. Defaults to false.
Behavior5/5

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

No annotations provided, so description carries full burden. It discloses the orchestration process, output shape (success, plan, results, summary, errors), error behavior (step failures non-aborting), and prerequisites. This is comprehensive for behavioral understanding.

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?

Well-structured with purpose upfront, bulleted examples, and separate sections for prerequisites, returns, and errors. Slightly verbose but each sentence adds value; could be trimmed marginally without losing content.

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 prerequisites, behavior, error handling, and provides multiple intent examples. Lacks details on worker registration and rate limits, but given the tool's complexity and absence of output schema, it is fairly complete for an agent to use effectively.

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?

Input schema covers 100% of parameters with detailed descriptions and examples. The description adds extra intent examples but does not significantly enhance parameter understanding beyond 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 it runs the agent orchestrator with a natural language design intent, classifying tasks, building plans, and executing them. It distinguishes from many specific sibling tools (e.g., create_spec, generate_code) by being a high-level orchestrator that can dispatch to those agents.

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?

Provides prerequisites (no Figma connection needed for spec/code tasks, bridge needed for Figma-touching tasks) and advises specificity. However, it does not explicitly compare to sibling tools or state when to use compose over more specific alternatives, leaving some ambiguity.

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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