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Read a design system

get_design

Retrieve a brand's design specification or a single section to align generated UI with a chosen visual style, replacing generic AI output.

Instructions

Return a DESIGN.md, or - strongly preferred - one section of it. Always pass section unless you genuinely need the whole document: these files average 28KB (~8k tokens) and a single section is typically under 10% of that.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sectionNoOne section, e.g. "Colors", "Typography", "Components". Matches loosely.
design_idYesNamespaced id, e.g. "getdesign:stripe".

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations provided, the description carries the burden of behavioral disclosure. It usefully reveals the large average size (~8k tokens) and that sections are typically under 10% of the file, making the cost tradeoff transparent. It does not discuss errors or return format, but the core behavior is clear.

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?

Two concise sentences deliver the key information up front: what is returned, the preferred mode, and the cost-based reason. Every sentence earns its place with no filler.

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 simple read tool with full schema coverage and no output schema, the description explains both the return value and the key usage consideration. It is slightly incomplete in not addressing siblings like get_design_sections, but the core calling context is sufficiently covered.

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 coverage is 100%, so the parameters are already documented. The description adds important practical meaning by recommending the `section` parameter be used almost always, effectively clarifying that `section` is optional in schema but strongly preferred in practice.

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 what the tool does: return a DESIGN.md file, or preferably one section of it. This is specific enough to distinguish the core operation, though it does not explicitly differentiate itself from siblings like get_design_sections or get_project_design.

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

Usage Guidelines5/5

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

Provides explicit, actionable guidance: always pass `section` unless the whole document is genuinely needed, with a concrete token-cost rationale. This is exactly the kind of when-to-use guidance an agent needs.

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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curl -X GET 'https://glama.ai/api/mcp/v1/servers/sam-rubinstein/frontend-design-mcp'

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