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by io.github.snayyar00
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Validate ARIA in HTML

check_aria
Read-onlyIdempotent

Validate ARIA attribute + accessible name/role/value usage in an HTML snippet. Runs axe-core cat.aria and cat.name-role-value rules (aria-* attribute correctness, role validity, required parents/children, aria-hidden-focus, accessible names). Returns violations (high-confidence) and incomplete (needs human review, e.g. dangling ARIA references — do NOT auto-fix). Nodes cap at 5 per rule by default — every rule reports nodesTotal + truncated; raise nodeLimit (max 50).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
htmlYesHTML to test for ARIA correctness
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.
nodeLimitNoMax nodes returned per rule (default 5, max 50)
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. First observed

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare this safe/read-only/idempotent, and the description adds meaningful behavior beyond that: it explains the violations vs incomplete split, warns that dangling ARIA references must not be auto-fixed, and discloses the per-rule node cap and truncation reporting. It stops short of exhaustive output field detail, but for an unannotated return surface this is strong.

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 core purpose, then layered with rules, return shape, and cap behavior in three dense sentences. Nearly every clause earns its place, though the parenthetical rule list and cap details add length that could be trimmed slightly.

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?

With no output schema and rich annotations, the description correctly carries the return-value burden by defining violations vs incomplete and truncation behavior. Complete for correct invocation, though it could say more about severity fields or how to act on incomplete results.

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 description coverage is 100%, so the schema already documents html, context, llm_model, nodeLimit, and conversation_id. The description's note on nodeLimit default and max restates what the schema already says, adding no new semantics. Baseline 3 is correct.

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 (validate) and precise resource scope (ARIA attribute plus accessible name/role/value usage in an HTML snippet), then enumerates the rule categories covered. Distinguishable from siblings like check_color_contrast or scan_html 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 Guidelines3/5

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

The description makes the operating context clear (ARIA correctness in an HTML snippet) and implicitly routes away from fixing via 'do NOT auto-fix', but never explicitly names when to prefer this over scan_html, scan_page, or verify_fix. Usage is implied rather than spelled out.

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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