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A11y Expert MCP

A11y Expert MCP

by Elizabeth1979
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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get_pattern

Retrieve the WAI-ARIA Authoring Practices implementation pattern for any UI component, including code examples and keyboard interaction requirements.

Instructions

Get the accessible implementation pattern for a UI component.

Args:
    component: The component name (e.g., "modal", "tabs", "combobox",
               "carousel", "menu", "dialog", "tooltip", "alert",
               "switch", "listbox", "treeview", "slider", "feed",
               "grid", "disclosure", "landmarks", "headings", "buttons",
               "link", "spinbutton", "meter", "menu button").

Returns:
    Detailed WAI-ARIA Authoring Practices pattern with code examples,
    keyboard interaction requirements, and ARIA roles/states/properties.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "component": "<component>"
    },
    "name": "get_pattern"
  }
}

Input Schema

NameRequiredDescriptionDefault
componentYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It doesn't disclose whether the operation is read-only, requires authentication, or has side effects. The return content is described, but behavioral traits like nondestructive nature are omitted.

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 succinct and well-structured with 'Args:' and 'Returns:' sections. Every sentence adds value, and the main purpose is stated upfront without unnecessary words.

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 tool with one parameter, the description covers purpose, input examples, and return details. It lacks information on error conditions or prerequisites, but the output description is rich. Overall, it provides a complete picture for a simple retrieval tool.

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 description coverage is 0%, so the description must explain the parameter. It lists many example values and explains that the component name is expected. This adds significant meaning beyond the bare schema definition, though it doesn't specify constraints or error handling.

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 clearly states the tool's purpose: 'Get the accessible implementation pattern for a UI component.' The verb 'get' and resource 'implementation pattern' are specific, and it distinguishes from siblings like 'list_patterns' which likely handles listing patterns.

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

Usage Guidelines3/5

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

The description clarifies that the tool is used to fetch a detailed pattern for a given component, but it doesn't provide explicit when-not-to-use guidance or compare with sibling tools. It assumes the user knows to call it when they need specific ARIA practices, missing exclusions.

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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