Skip to main content
Glama

Draw a shirt from words

draw_shirt

Draws a one-of-a-kind t-shirt design from a short idea in the person’s own words, in any language (3–140 characters), such as “a fox reading the morning paper”. For a shirt idea, a gift or merch. Makes four pictures in the shop’s dot-drawing style; returns the first as soon as it is ready, with the drawing’s id.

Input Schema

NameRequiredDescriptionDefault
promptYesWhat the drawing should show, in the person’s own words.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations declare it's not read-only, not idempotent, and not destructive, which is accurate. The description adds valuable behavioral context beyond annotations: it generates four pictures, returns the first as soon as ready, and provides the drawing's id. It doesn't disclose whether this consumes credits or if the other three images are accessible later, but the async/streaming behavior is well explained.

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?

One dense sentence covering purpose, input, constraints, use case, and output behavior. Front-loaded with the core function. Could be slightly more structured with separate sentences, but every clause earns its place.

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?

Given the simplicity (1 param, no output schema, clear annotations), the description covers what's needed: what it does, input format, behavior, and return. Missing details about image access after the first return and any credit usage, but those are minor for an agent to call correctly.

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 coverage is 100%, so the schema fully documents the single parameter including length constraints and description. The description adds the example and multilingual capability, but this is marginal value over the schema baseline.

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?

States a specific verb ('Draws') and resource ('t-shirt design') with the exact input form ('from a short idea in the person's own words'). It clearly distinguishes itself from siblings like draw_from_photo by specifying text-based generation.

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 usage context ('For a shirt idea, a gift or merch') but does not explicitly state when to use this versus siblings like draw_from_photo or print_words. The examples and length constraints imply appropriate use cases without naming alternatives.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources