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zcube

Penpot MCP Server

by zcube

query_shapes

Query and filter shapes by type, color, area, or text. Returns only specified fields for bulk edits like retheming or alignment.

Instructions

Query and filter shapes with multiple criteria (like grep for shapes). Useful for bulk operations like changing color themes, alignment, or filtering by area/type. Returns only the requested fields.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
maxXNoMaximum X position (area filter)
maxYNoMaximum Y position (area filter)
minXNoMinimum X position (area filter)
minYNoMinimum Y position (area filter)
typesNoFilter by shape types (e.g., ["text", "rectangle", "circle"])
fieldsNoFields to return: "position" (x,y,width,height), "colors" (fill,stroke), "text" (font props), "effects" (shadow,blur), "all". Default: ["position"]
fileIdYesFile ID
pageIdYesPage ID
fillColorNoFilter by fill color (HEX, e.g., "#FF0000")
fontFamilyNoFilter by font family (for text shapes)
namePatternNoFilter by name pattern (regex)
strokeColorNoFilter by stroke color (HEX)
textContentNoFilter by text content (case-insensitive substring match)
Behavior3/5

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

With no annotations, the description carries full burden for behavioral disclosure. It does mention 'Returns only the requested fields,' which is useful, but it omits details like pagination, read-only guarantees, or potential performance implications of a broad query.

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?

The description is two sentences, front-loaded with the main purpose and metaphor, followed by use cases and return behavior. Every word earns its place—excellent conciseness and structure.

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

Completeness3/5

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

For a query tool with 13 parameters, no output schema, and no annotations, the description gives high-level context but lacks critical specifics like result limits, ordering, or a definitive read-only statement. It is adequate but not fully complete.

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 100%, so the baseline is 3. The description adds minimal extra meaning beyond the schema, tying 'bulk operations' to parameter usage but not explaining individual parameters beyond what is already documented.

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 states the tool queries and filters shapes, using a memorable 'grep for shapes' metaphor. However, it does not explicitly distinguish itself from sibling tools like search_shapes, so it falls short of a 5.

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 provides clear use cases: 'bulk operations like changing color themes, alignment, or filtering by area/type.' It does not mention when not to use the tool or alternatives, but the context is strong enough for a 4.

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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