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Get contract as SKILL.md

designesy_skill_md
Read-onlyIdempotent

Get the Designesy SKILL.md: the agent-skill-format export of the design-system contract, for a user to save into .agents/skills/ or a system prompt so a coding agent builds UI to the contract (tokens, anti-patterns, rules, verification). Use this when the user wants the contract in that form. When NOT to use: for the raw contract JSON, use designesy_contract; for scoring, use designesy_score. Read-only: no side effects. Returns JSON: { kind: "published_document", source_url, media_type: "text/markdown", note, omitted_sections[], content }, where content is the SKILL.md markdown (tens of thousands of characters) with any section written as steps or a prompt for an AI agent replaced by a one-line marker and named in omitted_sections. No parameters.

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • removedInput schema / $schema
      Removed value: -"http://json-schema.org/draft-07/schema#"
  2. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnly/idempotent/no-destructive, but the description adds context the annotations cannot: it documents that sections written as agent steps or prompts are replaced with one-line markers, names them in omitted_sections, and warns the content runs tens of thousands of characters. That omission behavior is a genuine, non-obvious trait an agent must know before relying on the output.

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?

Front-loaded with the purpose and the when/when-not routing before the return-format detail, so the critical selection information lands first. It is longer than strictly necessary because the inline return-shape description is dense, but every sentence carries actionable information.

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?

With no output schema, the description carries the full burden of documenting the return value and does so precisely (kind, source_url, media_type, note, omitted_sections, content), including the truncation semantics. Combined with the routing guidance, an agent has everything needed to call it and interpret the result.

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?

The tool takes zero parameters and the schema is empty, so the baseline is 4. The description states 'No parameters' explicitly, which is correct and consistent with the schema, but adds no further parameter meaning to give.

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 and resource ('Get the Designesy SKILL.md') plus the exact export format ('agent-skill-format export of the design-system contract') and its intended destination (.agents/skills/ or a system prompt). It explicitly distinguishes itself from designesy_contract and designesy_score, so an agent can select it without opening any schema.

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?

Gives an explicit positive trigger ('Use this when the user wants the contract in that form') and an explicit negative trigger ('When NOT to use'), naming the correct alternatives for the excluded cases (designesy_contract for raw JSON, designesy_score for scoring). Nothing is left to inference.

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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