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by io.github.snayyar00
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Suggest a framework-aware fix

generate_ai_fix

Generate framework-aware fix alternatives for a specific accessibility issue. For color contrast issues, returns 3 alternatives (minimal, brand-aligned, high contrast); brand palette is auto-extracted from the live URL using our scanner if brandColors is omitted. For label/ARIA issues, returns 1-2 alternatives. Each alternative includes ready-to-paste code for the detected framework. NOTE: on this HOSTED server, localhost and private addresses are refused — it runs in our cloud and cannot reach your machine. Two ways to scan a local dev server: run the MCP locally (npx -y @webability/mcp, simplest — nothing leaves the machine), or open a tunnel (webability-tunnel --port 3000) and pass its URL as url together with the printed secret as tunnel_secret.

Input Schema

NameRequiredDescriptionDefault
urlNoPage URL — also used to auto-extract brand palette for contrast issues if `brandColors` is not provided.
htmlYesThe element's outerHTML — send at most ~600 chars
issueYesIssue object from scan_page (with selector, wcag, impact, message, fix.currentValue), or a plain-text issue description
contextNoParent element outerHTML for context (~400 chars)
frameworkYesCSS framework — use detect_framework first
llm_modelNoThe 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.
brandColorsNoBrand palette for brand-aligned suggestions. If omitted on a contrast issue with a `url`, auto-extracted via the scanner.
tunnel_secretNoSecret printed by `webability-tunnel`. Required when `url` is a tunnel URL; the URL alone will be refused by the relay. Ignored otherwise.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • removedInput schema / properties / conversation_id
      Removed value: -{
-  "description": "Echo 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.",
-  "type": "string"
-}
  2. Changed1 schema field changed
    • changedInput schema / required
      Previous value: -[
-  "issue",
-  "html",
-  "framework",
-  "llm_model"
-]New value: +[
+  "issue",
+  "html",
+  "framework"
+]
  3. First observed

TDQS

A4.5/5.0
Behavior5/5

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

Beyond the annotations, it discloses a hard reachability constraint (localhost/private addresses refused on the hosted server), the reason (cloud-hosted, cannot reach your machine), and two concrete workarounds including the secret requirement. This is exactly the kind of runtime behavior an agent cannot infer from annotations alone.

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-loads the core capability with zero waste, then adds operational caveats in a clearly separated NOTE. Somewhat long, but every sentence (contrast-vs-ARIA branching, localhost refusal, tunnel secret) carries information the agent needs.

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?

An 8-param tool with no output schema, yet the description covers output cardinality, auto-extraction behavior, and the hosted-server constraint. The remaining gaps (e.g., what happens on framework mismatch, latency/LLM variability) are minor.

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 already 100%, so baseline is 3. The description adds genuine meaning: brandColors auto-extracted from live URL when omitted, and tunnel_secret required for tunnel URLs. It exceeds the schema on those two params, though it does not elaborate on llm_model or context.

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?

Specific verb ('generate') plus resource ('framework-aware fix alternatives for a specific accessibility issue'), with output shape made concrete (3 contrast alternatives, 1-2 label/ARIA alternatives, ready-to-paste code). This clearly distinguishes it from siblings like verify_fix or detect_framework.

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 clear context for when each branch applies (contrast vs label/ARIA issues) and detailed guidance for the local-dev-server case, but does not name sibling alternatives (e.g., detect_framework first, verify_fix to validate) inside the description itself. Strong context, no explicit 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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