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tathagat22

Plumb (plumb-mcp)

by tathagat22

Plumb · design (build)

plumb_design

Validate and compile a DSL document into Figma auto-layout frames with text, images, and icons. Supports create, sync, and dry-run modes with optional design brief intake.

Instructions

Author a design from a high-level Plumb Design DSL document and BUILD it into the paired Figma file. This is the write direction: the DSL (semantic pages → sections → blocks + brand tokens) is validated, compiled DOWN to the PDS IR, lowered to a Figma-native emit plan, and executed by the plugin (auto-layout frames, text, images, icons). Returns the created node ids keyed by authored element handle — the join key you then feed to plumb_review / motion. Pass a brief to record intent (audience, tone, brand) alongside the build. Use dryRun: true to compile + validate without touching Figma.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
docNoA Plumb Design DSL document (version "1": brand + pages of semantic sections). Validated against DesignDocSchema.
modeNo`create` (default) — fresh nodes. `sync` — idempotent re-apply keyed on stable plumbKey (authored element handle); pair with `prune` to delete stale nodes.
briefNoOptional design-brief intake — records author intent alongside the build.
pruneNosync only — delete built nodes no longer in the design.
dryRunNoCompile + lower only; do NOT touch Figma. Returns plan stats + warnings so you can sanity-check before building.
revealNoScroll + select the built root in Figma when done.
targetNoWhere to build. Defaults to a new top-level frame on the current page.
pageWidthNoLayout width hint for pages that don't set their own.
Behavior5/5

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

Fully discloses behavior: write operation, the pipeline (validate, compile, lower, execute), side effects (touches Figma unless dryRun), output format (node ids keyed by handle). No contradiction with annotations.

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?

Concise, front-loaded with core action, well-structured without fluff. Each sentence adds value.

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

Completeness5/5

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

Given the complexity (8 params, nested objects, no output schema), the description is complete—covers purpose, process, parameters, output, and integration with siblings.

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?

All 8 parameters have schema descriptions (100% coverage), and the description adds contextual meaning, e.g., explaining dryRun returns plan stats, and tying parameters like mode and prune to the workflow.

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 authors a design from a DSL and builds it into Figma. It specifies the write direction and distinguishes from siblings by mentioning the output feeds into plumb_review/motion.

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 clear context for use (authoring/building, dryRun for sanity-check, modes create/sync) but does not explicitly exclude other tools or give when-not 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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