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hig-mcp

by aka-kika

hig_fetch

Read-onlyIdempotent

Fetch Apple Human Interface Guidelines content as clean Markdown for AI grounding. Provide a HIG path, slug, or URL to get markdown, source, and canonical Apple URL.

Instructions

Fetch current HIG prose as clean Markdown via sosumi.ai.

Thin convenience wrapper so this one server covers both tokens and prose. sosumi.ai renders Apple's DocC pages to AI-friendly Markdown. Returned text is for grounding only: summarize and cite the canonical Apple URL, do not reproduce it wholesale. Override the backend with HIG_SOSUMI_BASE.

Args: params.path: HIG slug, /design/... path, or full developer.apple.com URL.

Returns: dict: markdown content, the sosumi source, and the canonical Apple URL.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, idempotentHint, and nondestructive behavior; the description adds meaningful behavioral context beyond that: the returned text is for grounding only, the backend is overridable via HIG_SOSUMI_BASE, and the return value includes source and canonical URL details. No contradiction with annotations.

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 compact and well-structured: a clear purpose sentence, a short context sentence, a licensing restriction, an environment override, then Args and Returns sections. Every sentence earns its place, and the structure is scannable for an agent.

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?

For a single-read-only-parameter tool with annotations covering safety and a schema covering the input, the description supplies everything else needed: return shape, source behavior, and licensing constraints. There is no significant invocation-relevant gap.

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?

The input schema already documents path with examples such as 'materials' and '/design/human-interface-guidelines/color'. The description's Args line restates the same meaning ('HIG slug, /design/... path, or full developer.apple.com URL') with little additional semantic value, so the baseline schema coverage is doing the work.

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 opens with a specific verb and resource: 'Fetch current HIG prose as clean Markdown via sosumi.ai.' It also distinguishes the prose-fetching role from token retrieval with 'so this one server covers both tokens and prose', making sibling differentiation clear.

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 clearly implies this tool is for HIG prose, not tokens, and gives explicit handling guidance: 'summarize and cite the canonical Apple URL, do not reproduce it wholesale.' It does not explicitly name sibling alternatives as exclusions, but the 'tokens and prose' contrast gives practical selection context.

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