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webability

by io.github.snayyar00
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List accessibility rules

get_rules
Read-onlyIdempotent

List accessibility rules from both engines — axe-core (104) and the WebAbility detectors (90+) — with optional filters. Every rule carries fixability (mechanical | contextual | visual) and a fix op template, so you can pick the rules worth auto-fixing before scanning. Returns ruleId, engine, description, help, helpUrl, tags/wcag, fixability, fix.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ruleNoOnly rules whose id contains this text (e.g. "color-contrast", "label"; case and -/_ insensitive). Also accepted: ruleId, id, query.
tagsNoaxe tag filter (e.g. ["wcag21aa"], ["best-practice"], ["cat.aria"]). WebAbility rules match on their WCAG criterion tag (e.g. "wcag143").
engineNoWhich engine's rules to list (default all)
contextYesExplain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as "a user", "the customer", or "an account". Example: "Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution."
llm_modelYesThe exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. "claude-opus-4-8", "gpt-5.2"). Used for analytics only. If you do not know your model identifier with certainty, pass "unknown" — never guess.
fixabilityNoOnly rules of this fixability tier
conversation_idNoEcho the conversation_id from the server's previous response. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / rule
      Added value: +{
+  "description": "Only rules whose id contains this text (e.g. \"color-contrast\", \"label\"; case and -/_ insensitive). Also accepted: ruleId, id, query.",
+  "type": "string"
+}
  2. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnly/idempotent/non-destructive, so the safety bar is low. The description adds genuinely useful behavior: the two engine sources, approximate rule counts, the fixability taxonomy, and the exact return field set — none of which the annotations provide.

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 tightly packed sentences, front-loaded with the core action and scope, followed by the value proposition and return fields. No filler sentences; every clause carries information.

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?

No output schema exists, and the description compensates by enumerating returned fields. For a 7-parameter read tool it covers purpose, filters, and returns adequately; the only gap is the multi-call conversation_id workflow, which the schema itself documents.

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 baseline is 3, but the description adds meaning beyond it by explaining the fixability tiers (mechanical | contextual | visual) and the fix op template concept, helping an agent understand why to filter on those values.

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 (List) and resource (accessibility rules) and adds the distinguishing scope: rules from both the axe-core and WebAbility engines. This is clearly a rule-catalog listing tool, easily separated from scanning siblings like scan_page or check_aria.

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?

Gives a concrete use context — 'pick the rules worth auto-fixing before scanning' — which implies this is a pre-scan reconnaissance step. It does not name an alternative tool or state when-not to use it, so it stops short of full when/alternative guidance.

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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