Skip to main content
Glama
tathagat22

Plumb (plumb-mcp)

by tathagat22

Plumb · verify

plumb_verify
Read-onlyIdempotent

Compare tagged UI elements against their Figma design, returning structured deltas for size, style, color, text, and asset fidelity with coverage tracking.

Instructions

Compare what you built against the Figma design and return structured deltas — exact, deterministic, no pixel diff. After rendering, for every element you tagged data-plumb-id="<el>" (or the globally-unique data-plumb-id="<path>" for deeply nested DOM), collect: • box — getBoundingClientRect() → { x, y, w, h } • styles — a subset of getComputedStyle: backgroundColor, color, fontFamily, fontSize, fontWeight, lineHeight, padding{Top,Right,Bottom,Left}, gap, flexDirection, justifyContent, alignItems, borderRadius, borderColor, borderWidth, opacity, textDecorationLine, boxShadow, backdropFilter • text — textContent for TEXT nodes • asset — for image/icon/logo nodes (assetId or vector): the data-plumb-asset="" you rendered, plus img:true when it is a real / (not a redrawn div). Verify errors on a visual node rendered with no real asset, so a redrawn/omitted logo lowers the score. Pass them as rendered. The tool joins by el (loose) or path (strict) and returns deltas like { kind:'size.w', expected:528, actual:530, severity:'warn' }. The response also includes coverage — how many PDS els in the subtree were actually tagged, plus an untagged list so you know what to add next round. QA-strict on appearance — colour (ΔE2000), icon/asset fidelity, box-shadow, backdrop-filter (glass) and the rest flag even small misses — but content-aware on text: a mismatch on placeholder/template copy (lorem, generic labels, numeric stubs, copy-pasted cells) is advisory info (kind 'text.placeholder', doesn't dent the score), since you're meant to swap real content in. Real UI labels still warn (kind 'text.chars'). ok=true means no errors; warns are differences you may have meant.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idNoScreen id.
urlNoPaste a full Figma URL — fileKey and node-id are auto-extracted.
nameNoScreen name (plugin path).
depthNoPDS depth to fetch. Default 12 — deep enough for most screens.
fileKeyNoFile key (REST path).
renderedYesEach element you tagged data-plumb-id="<el>".
viewportNoBrowser viewport size (informational).
tolerancesNoOverride default thresholds.
Behavior5/5

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

Annotations declare readOnlyHint=true and idempotentHint=true, and the description adds rich behavioral context: deterministic, no pixel diff, error on missing assets, content-aware on placeholder text. This substantially extends beyond the annotations by explaining exact return structure and coverage metrics.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is verbose and contains multiple paragraphs with extensive detail. While the information is valuable, it could be more concise. The main purpose is front-loaded, but subsequent sections could be streamlined for easier scanning.

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?

For a tool with 8 parameters, nested objects, and no output schema, the description is quite comprehensive. It covers input format, delta kinds, coverage, and error conditions. However, it lacks a concrete example of the output, which would enhance completeness given the absence of an output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so baseline is 3. The description adds value by explaining the 'rendered' parameter in detail (how to tag elements, what properties to include) and the 'tolerances' parameter (thresholds). It also clarifies the 'asset' field and 'img' boolean, which are not fully explained in the schema.

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 explicitly states the tool's purpose: 'Compare what you built against the Figma design and return structured deltas'. It uses a specific verb ('compare') and resource ('Figma design'), clearly distinguishing it from sibling tools like plumb_diff (which likely compares two designs) and plumb_audit.

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 explains when to use the tool: after rendering, to verify against Figma. It details prerequisites (data-plumb-id tagging) and behavioral nuances (content-aware on text, QA-strict on appearance). It does not explicitly list alternative tools or state when not to use, but the context is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/tathagat22/plumb-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server