WCAG Accessibility MCP
This server gives AI agents automated WCAG accessibility auditing and contrast analysis for web interfaces.
Audit live URLs:
audit_urlrenders a public or local web page in headless Chromium and returns prioritized WCAG findings with DOM evidence.Audit raw HTML:
audit_htmltests unhosted or generated HTML in an isolated browser before deployment.Audit local files:
audit_filechecks.html/.htmfiles inside the allowed project root.Check color contrast:
check_contrastevaluates foreground/background pairs against WCAG AA/AAA thresholds for text size and weight.Suggest contrast fixes:
suggest_contrast_fixfinds the nearest passing color adjustment.Explain accessibility rules:
explain_issuegives remediation steps for axe rule IDs.Get WCAG checklists:
get_wcag_checklistreturns success criteria for WCAG 2.0/2.1/2.2 A/AA/AAA profiles, separating automated-partial from manual checks.Support multiple standards: wcag2a through wcag22aaa and best-practice profiles, defaulting to wcag22aa.
Return actionable evidence: issues include affected elements, computed styles, corrections, and guidance for what still needs manual review.
Operate read-only: all tools are safe for agents to invoke without modifying the audited system.
WCAG Accessibility MCP
A WCAG accessibility testing MCP for AI agents, Codex, Claude Code, and automated UI audits.
WCAG Accessibility MCP gives AI coding and design agents a real accessibility feedback loop. It renders web interfaces in Chromium, runs axe-core, checks color contrast, maps findings to WCAG guidance, and returns structured evidence that an agent can use to fix accessibility issues before a UI is treated as complete.
It is designed for people building with AI: product designers, design engineers, frontend developers, AI product teams, and anyone using agents to generate or review web interfaces.
This is an automated testing aid, not a WCAG certification service. It catches many common failures, but full accessibility review still requires human testing for keyboard behavior, focus order, screen readers, zoom, motion, content quality, and assistive-technology behavior.
Why This Exists
AI-generated interfaces often look polished but fail basic accessibility checks: weak contrast, missing labels, broken semantic structure, poor ARIA usage, inaccessible forms, and hidden keyboard issues.
This MCP helps agents stop guessing. Instead of saying “follow WCAG,” it gives them testable feedback:
what failed
where it failed
which element was affected
which WCAG-related rule applies
what needs to be fixed
which checks still require manual review
Related MCP server: Accessibility MCP Server
What It Can Do
Tool | What it does |
| Audits a rendered web page URL |
| Audits supplied HTML before it is hosted |
| Audits a local |
| Experimental: audits one target across named viewports and groups findings by stable fingerprints |
| Experimental: compares baseline and candidate audit results without rerunning the browser |
| Experimental: reports browser selection and setup guidance without launching Chromium |
| Experimental: captures screenshot evidence and optionally runs local OCR |
| Experimental: inspects exported Figma, Claude Design, or generic design-node data before implementation |
| Calculates WCAG contrast for a foreground/background color pair |
| Suggests a passing color adjustment |
| Explains an accessibility rule and how to fix it |
| Returns the complete WCAG checklist with automated/manual coverage |
Standards Coverage
The server supports these profiles:
wcag2awcag2aawcag2aaawcag21aawcag21aaawcag22aawcag22aaabest-practice
AA profiles include Level A and AA criteria. AAA profiles include Level A, AA, and AAA criteria.
The default profile is wcag22aa.
For WCAG 2.2:
AA includes 55 required success criteria.
AAA includes 86 required success criteria.
An automated axe mapping means partial automated coverage. It does not mean the whole WCAG success criterion has been fully tested.
Install
Requirements:
Node.js 20 or newer
Chrome or Edge recommended
Windows, macOS, or Linux
Install from npm:
npm install -g wcag-accessibility-mcpOr run directly:
npx -y wcag-accessibility-mcpIf the server starts correctly, you should see:
wcag-accessibility-mcp is running over stdioUse With Claude Code
Add the MCP server:
claude mcp add --scope user wcag-accessibility -- npx -y wcag-accessibility-mcpCheck that it is connected:
claude mcp listTest prompt:
Use the wcag-accessibility MCP tool check_contrast. Check #999999 on #ffffff for WCAG AA normal text. Do not calculate manually.Expected result:
The contrast ratio is about 2.85:1, which fails WCAG AA for normal text.Use With Claude Desktop
Open the Claude desktop config file on Windows:
notepad "$env:APPDATA\Claude\claude_desktop_config.json"Add this inside mcpServers:
{
"mcpServers": {
"wcag-accessibility": {
"command": "C:\\Program Files\\nodejs\\npx.cmd",
"args": [
"-y",
"wcag-accessibility-mcp"
]
}
}
}If you already have another MCP server, keep it and add wcag-accessibility as a second entry.
Then fully quit Claude Desktop and reopen it.
Use With Codex
Add the MCP server:
codex mcp add wcag-accessibility -- npx -y wcag-accessibility-mcpTest prompt:
Use the wcag-accessibility MCP to check the contrast of #999999 on #ffffff at WCAG AA.Example Agent Prompts
Audit this HTML for WCAG 2.2 AA issues. Prioritize critical and serious issues, explain each fix, then list what still needs manual review.Use wcag-accessibility to audit this local HTML file at desktop and mobile widths. Fix the accessibility issues in the code and rerun the audit.Inspect this Figma or Claude Design snapshot for accessibility risks before implementation, then verify the rendered HTML with audit_matrix.Capture a screenshot of this page and run OCR only if local OCR is configured. Use OCR findings as visual evidence, not as WCAG conformance.Check whether #FF3B12 on #F5F2EC passes WCAG AA for normal text. If it fails, suggest the nearest passing foreground color.Give me the WCAG 2.2 AAA checklist and separate automated checks from manual checks.Local Development
Clone the repository:
git clone https://github.com/aditya-ariosity/wcag-accessibility-mcp.git
cd wcag-accessibility-mcpInstall dependencies:
npm installBuild:
npm run buildRun tests:
npm run test:unitRun browser integration and end-to-end tests:
npm run test:browsernpm test is the deterministic browser-free unit suite. Use npm run test:browser when the machine has a working Chrome, Edge, Chromium, or bundled browser setup.
The CLI can write JSON, Markdown, or SARIF reports and compare a baseline:
npm run audit -- --url http://localhost:3000 --format json --out report.json
npm run audit -- --url http://localhost:3000 --baseline baseline.json --format mdStart the stdio MCP server:
npm startStart the optional HTTP transport:
npm run start:httpThe local HTTP endpoint is:
http://127.0.0.1:3000/mcpHealth check:
http://127.0.0.1:3000/healthEnvironment Variables
All environment variables are optional for normal local use.
Variable | Default | Purpose |
| Auto-detected | Absolute path to Chrome, Chromium, or Edge |
| Current working directory | Restricts local file audits to a specific folder |
|
| Allows localhost/private-network URL audits in trusted environments |
|
| Enables local file audits over HTTP in trusted deployments |
|
| Maximum concurrent Chromium audits |
|
| Timeout for the audit phase |
|
| Disables Chromium sandboxing only when the runtime requires it |
|
| Local OCR executable used by |
|
| Advertises the in-progress matrix, screenshot, diagnostics, and design-snapshot tools |
|
| Allows local file audits to load file-based images/stylesheets beneath the allowed root |
|
| HTTP bind address |
| Localhost names | Allowed Host headers for HTTP mode |
|
| HTTP JSON body limit |
|
| HTTP transport port |
Security Notes
The stdio server is meant to run locally with the same permissions as the agent using it.
The HTTP transport is a deployment building block, not a public hosted service by itself. Do not expose it directly to the public internet without authentication, TLS, rate limits, request-size limits, outbound network controls, and a low-privilege runtime.
By default:
tools are read-only
HTTP mode blocks private network URL targets
local file audit over HTTP is disabled
local file audit is restricted to the allowed root
Chromium sandboxing remains enabled unless explicitly disabled
Published Links
GitHub: https://github.com/aditya-ariosity/wcag-accessibility-mcp
MCP Registry name:
io.github.aditya-ariosity/wcag-accessibility
Current Scope
The stable release currently audits rendered web interfaces and HTML. The evidence core reports criterion-level coverage and keeps unresolved manual requirements visible.
The stable release does not yet inspect:
native mobile apps
PDFs
canvas-only interfaces
video captions
raw screenshots without HTML
raw Claude Design pixels
Matrix, screenshot/OCR, and design-snapshot modules are experimental and require A11Y_MCP_ENABLE_EXPERIMENTAL_TOOLS=true. Direct Figma plugin inspection, persistence, automatic fixes, and deeper design-tool adapters remain future work.
License
MIT
Available Tools
7 toolsaudit_fileAudit a local HTML fileARead-onlyIdempotent
Render a local .html or .htm file beneath the configured allowed root and return accessibility findings and corrections. Read-only.
| Name | Required | Description | Default |
|---|---|---|---|
| width | No | Viewport width in CSS pixels. | |
| height | No | Viewport height in CSS pixels. | |
| filePath | Yes | Absolute path, or path relative to the server working directory, to an HTML file. | |
| standard | No | WCAG ruleset to test. Use best-practice to include additional axe guidance. | wcag22aa |
| maxIssues | No | Maximum violation groups returned; summary totals still cover the full run. | |
| maxNodesPerIssue | No | Maximum affected elements returned per rule. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint, idempotentHint, and destructiveHint. The description adds the constraint that files must be 'beneath the configured allowed root', which is a behavioral safety detail not present in any structured field. It also reinforces the read-only nature. No contradictions 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise: two short sentences that front-load the core purpose ('Render a local .html or .htm file') and key constraints ('beneath the configured allowed root', 'Read-only'). No fluff or redundant detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 6 parameters (fully documented in schema), annotations, and no output schema, the description is largely complete. It covers the file type constraint, the allowed root security boundary, the read-only nature, and the expected output (accessibility findings and corrections). Missing is explanation of what 'render' entails (e.g., headless browser, JavaScript execution), but this is minor and compensated by the schema details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so each parameter (filePath, width, height, standard, maxIssues, maxNodesPerIssue) already has a description in the schema. The tool description adds no additional information about parameters beyond what the schema provides. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Render' and 'return accessibility findings and corrections') and resource ('local .html or .htm file beneath the configured allowed root'). It distinguishes itself from sibling tools like audit_url (which works on URLs) by explicitly specifying 'local file' and the file path restriction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not provide any explicit guidance on when to use this tool versus the available siblings (audit_url, audit_html, etc.). It does not state alternatives, prerequisites (e.g., file must be accessible), or exclusion criteria (e.g., not for raw HTML strings). The purpose implicitly suggests local files, but no clear usage context is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
audit_htmlAudit an HTML documentARead-onlyIdempotent
Render supplied HTML in an isolated headless browser and return WCAG findings plus evidence and suggested corrections. Use for generated UI before it is hosted. Read-only.
| Name | Required | Description | Default |
|---|---|---|---|
| html | Yes | Complete HTML document or fragment to render and test. | |
| width | No | Viewport width in CSS pixels. | |
| height | No | Viewport height in CSS pixels. | |
| baseUrl | No | Optional http(s) base URL used to resolve relative assets. | |
| standard | No | WCAG ruleset to test. Use best-practice to include additional axe guidance. | wcag22aa |
| maxIssues | No | Maximum violation groups returned; summary totals still cover the full run. | |
| maxNodesPerIssue | No | Maximum affected elements returned per rule. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the agent knows this is a safe, read-only operation. The description adds 'isolated headless browser' and 'Read-only' which align with and reinforce the annotations. No contradictions. The description provides modest additional context beyond the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with no redundant or filler content. The first sentence clearly states the action and outcome, and the second provides usage guidance and a read-only note. Every part earns its place, making it highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 7 parameters and no output schema, the description covers the core purpose, usage context, and safety (read-only). It mentions the output type (WCAG findings, evidence, corrections) at a high level. However, it does not explain how parameters like standard or maxIssues influence behavior, though the schema descriptions handle those details. The description is sufficiently complete for an agent to use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so each parameter already has a description in the input schema. The tool description does not add specific parameter-level details, but it does describe the output (WCAG findings, evidence, corrections), which indirectly informs parameter usage. Baseline 3 is appropriate given full schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Render') and resource ('supplied HTML') with a clear outcome ('return WCAG findings plus evidence and suggested corrections'). It also distinguishes from sibling tools like audit_url and audit_file by stating 'Use for generated UI before it is hosted', making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says 'Use for generated UI before it is hosted', which provides clear context for when to use this tool. While it does not explicitly list alternatives or when not to use, the sibling tool names (audit_url, audit_file) imply the distinction, and the guidance is sufficient for an agent to select this over siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
audit_urlAudit a rendered URLARead-onlyIdempotent
Load a public or local web page in headless Chromium, run axe-core, and return prioritized WCAG findings, DOM evidence, computed styles, and contrast corrections. Read-only.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Absolute http:// or https:// URL to render and test. | |
| width | No | Viewport width in CSS pixels. | |
| height | No | Viewport height in CSS pixels. | |
| standard | No | WCAG ruleset to test. Use best-practice to include additional axe guidance. | wcag22aa |
| maxIssues | No | Maximum violation groups returned; summary totals still cover the full run. | |
| maxNodesPerIssue | No | Maximum affected elements returned per rule. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint true, and destructiveHint false. The description adds context: it uses headless Chromium, is read-only (reinforcing safely), and lists concrete outputs (WCAG findings, DOM evidence, computed styles, contrast corrections). No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence front-loaded with the primary action and outputs, then ends with 'Read-only.' Every part is essential and no filler. Excellent structure and brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 6 parameters (100% schema coverage), rich annotations, and no output schema, the description provides enough context for an AI agent to select and invoke the tool correctly. It covers inputs, behavior, and safety. Slight deduction because it doesn't mention pagination or error handling for invalid URLs, but overall complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description adds value beyond schema by summarizing the overall return structure (prioritized findings, evidence, contrast corrections), but doesn't detail individual parameter behaviors beyond what schema provides. Slight extra context earns a 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool loads a page, runs axe-core, and returns prioritized WCAG findings with specific outputs like DOM evidence and computed styles. It distinguishes itself from siblings like audit_html and audit_file, which operate on different input types.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implicitly indicates usage for accessibility auditing of rendered URLs and mentions 'Read-only.' However, it does not explicitly contrast with siblings like check_contrast or explain_issue, nor does it specify when not to use this tool (e.g., for non-public pages or HTML fragments).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
check_contrastCheck a color pairARead-onlyIdempotent
Calculate the WCAG contrast ratio for foreground and background colors and determine whether the pair passes for the supplied text size and weight.
| Name | Required | Description | Default |
|---|---|---|---|
| level | No | AA | |
| background | Yes | Background CSS sRGB color such as white, #ffffff, rgb(), hsl(), or color(srgb ...). | |
| fontSizePx | No | ||
| fontWeight | No | ||
| foreground | Yes | Foreground CSS sRGB color such as white, #767676, rgb(), hsl(), or color(srgb ...). |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint as false, so the tool's safe behavior is clear. The description adds value by explaining the return includes ratio and pass/fail for given text size/weight, but does not disclose any edge cases (e.g., handling of transparent colors, out-of-gamut colors) or limits (e.g., only sRGB). A 3 is appropriate as annotations carry the safety burden.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that front-loads the main action and includes key details (ratio, pass determination, text attributes). It is concise and efficient, though it could be slightly more scannable by breaking into two sentences. No redundancy is present.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 5 parameters (2 required) and no output schema, the description covers the main computation (contrast ratio and pass/fail) but omits specifics about return format (e.g., numeric ratio, boolean pass, object). For a calculation tool with moderate complexity, additional context about the scope (sRGB only) or limitations (e.g., does not handle transparency) would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 40%, so the parameter descriptions in the schema partially explain foreground/background colors as 'CSS sRGB color'. However, the tool description clarifies that both 'fontSizePx' and 'fontWeight' are used to determine the pass level, adding meaning beyond the schema's default values and constraints. It compensates for the missing schema descriptions on those parameters, though the level parameter's enum (AA, AAA) is already clear in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Calculate' and the resource 'WCAG contrast ratio', and distinguishes the tool from siblings like 'suggest_contrast_fix' by specifying the output includes both the ratio and a pass/fail determination based on text size and weight.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for checking color contrast but does not explicitly state when to use this tool versus alternatives like 'suggest_contrast_fix'. It lacks guidance on prerequisites or context (e.g., checking multiple pairs vs. one). Without sibling differentiation, the agent may not know when to choose this over other audit tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
explain_issueExplain an accessibility ruleARead-onlyIdempotent
Return implementation-focused remediation steps and the axe rule reference for a rule ID found in an audit.
| Name | Required | Description | Default |
|---|---|---|---|
| ruleId | Yes | axe rule ID such as color-contrast, button-name, or label. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate the tool is read-only and non-destructive. The description adds that it returns 'remediation steps' and 'axe rule reference', which is useful but does not elaborate on any potential limits or side effects. Given the annotations cover the safety profile, a 3 is appropriate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is one sentence that front-loads the core purpose ('return implementation-focused remediation steps') and adds the specific context about axe rule reference. No wasted words, but could be slightly more concise by removing 'the axe rule reference' redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that there is a single required parameter with full schema coverage and no output schema, the description sufficiently explains what the tool does and what it returns. It does not need to detail return structure since there's no output schema. The tool is straightforward and the description covers the key aspects.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already provides a detailed description for the 'ruleId' parameter (axe rule ID pattern), so schema coverage is 100%. The description does not add new parameter information beyond what the schema states, so a baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns remediation steps and a reference for a given rule ID. It specifies the verb 'return' and the resource 'implementation-focused remediation steps and axe rule reference', and distinguishes from sibling tools like 'check_contrast' which are more specific.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool should be used when a rule ID from an audit is known, but does not explicitly state when NOT to use it or suggest alternatives. For example, it doesn't mention that if you need to run a new audit you should use 'audit_url' instead.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_wcag_checklistGet the complete WCAG requirement checklistARead-onlyIdempotent
Return every success criterion required by a WCAG 2.0, 2.1, or 2.2 A/AA/AAA profile, with W3C references, axe rule mappings, and explicit automated-partial versus manual coverage. Use this to plan the checks that a browser audit cannot complete.
| Name | Required | Description | Default |
|---|---|---|---|
| standard | No | wcag22aa | |
| principle | No | ||
| evaluation | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the agent knows this is a safe read operation. The description adds context about the output (references, coverage types) but does not disclose additional behavioral traits such as response size, pagination, or performance implications. Given the rich annotations, a 3 is appropriate as the description adds some value beyond the structured fields.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences long, each adding value. The first sentence clearly defines what the tool returns, and the second sentence provides usage guidance. There is no wasted text, and no repetition of schema information. This is a model of conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that the tool has three optional parameters, no output schema, and moderate complexity (WCAG profiles with multiple dimensions), the description is too minimal. It does not mention that all parameters are optional, the default value for 'standard' (wcag22aa), or what the response structure looks like (e.g., list of criteria with metadata). For a checklist tool used for planning, users need more context about how the parameters affect the results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning the parameters (standard, principle, evaluation) have no descriptions in the schema. Although the parameter names and enum values are somewhat self-explanatory (e.g., 'standard' with values like 'wcag2aa'), the description does not explain how these parameters filter the checklist or fill the gap left by the schema. The description should at least describe the effect of providing these optional parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states it returns 'every success criterion required by a WCAG 2.0, 2.1, or 2.2 A/AA/AAA profile, with W3C references, axe rule mappings, and explicit automated-partial versus manual coverage.' This clearly distinguishes the tool from sibling audit tools (audit_url, audit_html, audit_file) which perform automated checking, and from issue-specific tools (check_contrast, etc.). The verb 'return' and resource 'checklist' are precise.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states 'Use this to plan the checks that a browser audit cannot complete,' giving a clear use case and implying differentiation from automated audit tools. However, it does not explicitly state when not to use this tool or mention alternative tools for other scenarios, which would earn a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
suggest_contrast_fixSuggest a passing colorARead-onlyIdempotent
Find the nearest black-or-white-directed foreground or background adjustment that reaches the selected WCAG contrast threshold. Returns a mathematical candidate, not an automatic edit.
| Name | Required | Description | Default |
|---|---|---|---|
| level | No | AA | |
| adjust | No | foreground | |
| background | Yes | Background CSS sRGB color such as white, #ffffff, rgb(), hsl(), or color(srgb ...). | |
| fontSizePx | No | ||
| fontWeight | No | ||
| foreground | Yes | Foreground CSS sRGB color such as white, #767676, rgb(), hsl(), or color(srgb ...). |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the tool is safe and non-destructive. The description adds key behavioral context: it returns a 'mathematical candidate' and explicitly states it is 'not an automatic edit.' This goes beyond annotations by clarifying that the user must apply the suggestion themselves, fully disclosing the tool's scope.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences long, each sentence earns its place. The first sentence states the core function concisely; the second clarifies the non-destructive, advisory nature. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has 6 parameters (2 required, 2 enums), moderate complexity. The description explains the what and the result, but lacks details like what happens when no adjustment can reach the threshold (returns null/error?). Additionally, there is no output schema, so the return format is left to experimentation. For a mathematical suggestion tool, the description is largely complete, but could mention error handling.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is only 33%, meaning the schema documents only two parameters (foreground, background) with descriptions. The description adds meaning by explicitly mentioning 'black-or-white-directed' adjustments and connecting parameters to WCAG thresholds. It does not detail each parameter beyond what the schema provides, but for the low coverage, it compensates well by explaining the tool's purpose. The enum parameters (level, adjust) are left for the schema to define, which is acceptable given the tool's focused purpose.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses specific verbs ('Find', 'adjustment') and resources ('foreground', 'background', 'WCAG contrast threshold'). It clearly identifies the tool's output as a mathematical candidate, distinguishing it from automatic edits and sibling tools like check_contrast or explain_issue.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not explicitly state when to use this tool versus alternatives like check_contrast or get_wcag_checklist. However, the mention of 'nearest...adjustment that reaches the selected WCAG contrast threshold' implies it is used when a user needs a suggestion to fix contrast, and 'Returns a mathematical candidate, not an automatic edit' distinguishes it from an auto-fix. No explicit exclusions are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
7 tool updates
v0.1.4- First observed
audit_file - First observed
audit_html - First observed
audit_url - First observed
check_contrast - First observed
explain_issue - First observed
get_wcag_checklist - First observed
suggest_contrast_fix
TDQS
Each tool targets a clear and distinct task: three different methods for auditing (by URL, HTML string, or file), a contrast checker, a contrast fix suggester, an issue explainer, and a checklist retriever. There is no overlap or ambiguity among them.
Tool names mostly follow a verb_noun pattern (audit_url, audit_html, audit_file, check_contrast, explain_issue, get_wcag_checklist). The only minor deviation is 'suggest_contrast_fix', which is a verb_verb_noun but still clear and consistent in style.
7 tools is an ideal number for this domain. Each tool covers a necessary function without unnecessary redundancy: three audit entry points, two contrast helpers, one explainer, and one checklist reference. The scope is well-scoped and focused on WCAG accessibility.
The tool set provides a complete lifecycle for accessibility auditing: multiple ways to ingest content for auditing, contrast analysis and remediation, issue explanation, and a checklist for manual coverage. There are no obvious gaps for the intended use case of assessing and fixing WCAG compliance.
Maintenance
Related MCP Connectors
Accessibility compliance for AI coding tools. WCAG 2.2 reviews with shared evidence.
Website QA for your coding agent: audit SEO, performance, security, accessibility over MCP.
- mcpOAuthcom.screenshotink
Screenshot, diff, audit and sitemap-capture any web page — 5 MCP tools for AI agents.
Scan URLs for WCAG 2.1 violations, generate AI fixes, and produce VPAT 2.5 compliance reports.
Related MCP Servers
- AlicenseAqualityCmaintenanceAn MCP server that enables LLMs to perform web accessibility testing against WCAG standards using Deque Axe-core API and Puppeteer.623391MIT
- AlicenseAqualityCmaintenanceProvides conversational, actionable accessibility testing for AI agents, including auditing, prioritization, and code-level fixes.2222MIT
- AlicenseNot gradedqualityDmaintenanceProvides AI agents with web accessibility analysis tools via MCP, enabling checks for alt text, heading hierarchy, color contrast, ARIA validation, and form accessibility.50MIT
- AlicenseAqualityCmaintenanceEnables auditing web pages for WCAG violations, applying deterministic fixes and PRs, all through MCP clients like Claude Desktop.7MIT
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/aditya-ariosity/wcag-accessibility-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server