Lighthouse MCP
The Lighthouse MCP Server provides comprehensive web performance auditing and analysis using Google Lighthouse, enabling detailed website assessments across multiple dimensions:
Performance Analysis: Run full Lighthouse audits, monitor Core Web Vitals, and evaluate performance scores
Accessibility Audits: Check WCAG compliance and get improvement recommendations
SEO Analysis: Conduct technical audits and receive optimization guidelines
Security Assessments: Perform scans for HTTPS, CSP, and vulnerabilities
Resource Optimization: Analyze JavaScript, CSS, images, and fonts
Device Comparison: Compare performance across mobile and desktop devices
Performance Budgets: Monitor and enforce custom performance thresholds
PWA Readiness: Assess Progressive Web App compliance
Unused Code Detection: Identify and reduce unused JavaScript
Reference Resources: Access guidelines, best practices, and framework guides
Integrated Analysis: Use prompts to analyze results, compare audits, and create optimization plans
Provides integration with Codecov for tracking code coverage metrics of the server codebase itself.
Integrates with GitHub for CI/CD workflows, enabling automated quality gates and performance regression detection through GitHub Actions.
Provides comprehensive web performance auditing and analysis capabilities using Google Lighthouse, enabling detailed website performance assessments, accessibility audits, SEO analysis, security checks, and Core Web Vitals monitoring.
Offers tools to check Progressive Web App readiness and provides PWA-specific analysis to evaluate whether a website meets PWA standards and requirements.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Lighthouse MCPaudit performance for https://example.com"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Lighthouse MCP Server
A Model Context Protocol (MCP) server that provides comprehensive web performance auditing and analysis capabilities using Google Lighthouse. This server enables LLMs and AI agents to perform detailed website performance assessments, accessibility audits, SEO analysis, security checks, and Core Web Vitals monitoring.
🌟 Key Features
🚀 Performance Analysis: Complete Lighthouse audits with Core Web Vitals, performance scores, and optimization recommendations
♿ Accessibility Audits: WCAG compliance checking and accessibility score analysis
🔍 SEO Analysis: Search engine optimization audits and best practice recommendations
🔒 Security Assessment: HTTPS, CSP, and security vulnerability scanning
📊 Resource Analysis: JavaScript, CSS, image, and font optimization opportunities
📱 Mobile vs Desktop: Comparative analysis across devices with throttling options
⚡ Core Web Vitals: LCP, INP, CLS monitoring with threshold checking
🎯 Performance Budgets: Custom performance thresholds and budget monitoring
🤖 Agentic Browsing: Lighthouse 13 audits for how well a page serves AI agents (WebMCP tools, agent accessibility tree, llms.txt)
🧩 Structured Output: Every tool declares an
outputSchemaand returns validatedstructuredContent, so clients get typed data instead of a JSON string to parse📚 Reference Resources: Built-in guidelines and best practices for web performance, accessibility, SEO, and security
Related MCP server: mcp-seo
🛠️ Requirements
Node.js 22.0.0 or newer
Chrome/Chromium browser (automatically managed by Lighthouse)
VS Code, Cursor, Windsurf, Claude Desktop, or any other MCP client
🚀 Getting Started
Install the Lighthouse MCP server with your preferred client using one of the configurations below:
{
"mcpServers": {
"lighthouse": {
"command": "npx",
"args": ["@danielsogl/lighthouse-mcp@latest"]
}
}
}Persistent Chrome Profiles (Login Sessions)
If you need authenticated sessions, launch with a persistent Chrome profile and run headed:
{
"mcpServers": {
"lighthouse": {
"command": "npx",
"args": [
"@danielsogl/lighthouse-mcp@latest",
"--profile-path",
"<profile-path>",
"--no-headless"
]
}
}
}You can pass extra Chrome flags with --chrome-flag, for example --chrome-flag=--disable-gpu.
If the flag value starts with -- and matches a known option name, prefer --chrome-flag=... to avoid parsing it as a top-level option.
Profile mode disables Lighthouse's storage reset so cookies and local storage persist between runs.
If --user-data-dir points to a missing directory, it will be created and treated as a fresh profile.
Set --profile-path to the Profile Path shown in chrome://version (e.g. .../Default).
Note: Chrome's remote debugging requires a non-default user data directory, so reuse a dedicated profile directory instead of the system default.
You can also pass --user-data-dir + --profile-directory separately if you prefer.
Attaching with --chrome-port alone does not preserve storage; include a profile flag to keep sessions.
CLI Options
Supported runtime flags for the MCP server:
--profile-path <path>: use the Profile Path fromchrome://version(auto-derives user data dir + profile name)--user-data-dir <path>: reuse a Chrome profile directory for persistent sessions--profile-directory <name>: select a profile within the user data dir--chrome-path <path>: explicit path to the Chrome/Chromium executable (overrides auto-detection; also respects theCHROME_PATHenvironment variable)--chrome-flag <flag>or--chrome-flag=<flag>: pass through extra Chrome flags (repeatable)--chrome-port <port>or--remote-debugging-port <port>: attach to an existing Chrome instance launched with remote debugging enabled--headless: force headless mode--no-headless: force headed mode
Logging
Lighthouse logs to stderr. The server keeps this at error so it does not flood your MCP
client's logs; set LIGHTHOUSE_LOG_LEVEL to silent, info or verbose when debugging
(for example when Chrome fails to launch).
LIGHTHOUSE_LOG_LEVEL=verbose npx @danielsogl/lighthouse-mcp@latestWSL2 / Custom Chrome Path
If the wrong Chrome binary is picked up (e.g. Windows Chrome instead of the Linux binary on WSL2), set the path explicitly:
# Via CLI flag
npx @danielsogl/lighthouse-mcp@latest --chrome-path /usr/bin/google-chrome
# Via environment variable
CHROME_PATH=/usr/bin/google-chrome npx @danielsogl/lighthouse-mcp@latestIn your MCP config:
{
"mcpServers": {
"lighthouse": {
"command": "npx",
"args": ["@danielsogl/lighthouse-mcp@latest", "--chrome-path", "/usr/bin/google-chrome"]
}
}
}E2E Smoke Test (Profile)
Run a real audit with a persistent profile (use an existing profile directory and log in once if needed):
npm run smoke:profile -- --url https://example.com \
--profile-path "<profile-path>" \
--no-headlessE2E Smoke Test (Attach to Existing Chrome)
Start Chrome with remote debugging enabled:
/path/to/GoogleChromeExecutable \
--remote-debugging-port=9222 \
--user-data-dir /path/to/chrome-profileReplace /path/to/GoogleChromeExecutable with your platform's Chrome/Chromium binary path.
Then attach Lighthouse to that instance:
npm run smoke:profile -- --url https://example.com --chrome-port 9222To preserve storage when attaching, pass the profile path so Lighthouse keeps cookies/local storage:
npm run smoke:profile -- --url https://example.com \
--chrome-port 9222 \
--profile-path "<profile-path>"Install in VS Code
You can also install the Lighthouse MCP server using the VS Code CLI:
# For VS Code
code --add-mcp '{"name":"lighthouse","command":"npx","args":["-y","@danielsogl/lighthouse-mcp@latest"]}'
# For VS Code Insiders
code-insiders --add-mcp '{"name":"lighthouse","command":"npx","args":["-y","@danielsogl/lighthouse-mcp@latest"]}'After installation, the Lighthouse MCP server will be available for use with your GitHub Copilot agent in VS Code.
Install in Cursor
Go to Cursor Settings → MCP → Add new MCP Server. Name it "lighthouse", use command type with the command npx @danielsogl/lighthouse-mcp@latest:
{
"mcpServers": {
"lighthouse": {
"command": "npx",
"args": ["@danielsogl/lighthouse-mcp@latest"]
}
}
}Install in Windsurf
Follow the Windsurf MCP documentation. Use the following configuration:
{
"mcpServers": {
"lighthouse": {
"command": "npx",
"args": ["@danielsogl/lighthouse-mcp@latest"]
}
}
}Install in Claude Desktop
Follow the MCP install guide, use the following configuration:
{
"mcpServers": {
"lighthouse": {
"command": "npx",
"args": ["@danielsogl/lighthouse-mcp@latest"]
}
}
}🔧 Available Tools
The Lighthouse MCP server provides the following tools for comprehensive web analysis:
🏁 Audit Tools
Tool | Description | Parameters |
| Run a comprehensive Lighthouse audit |
|
| Get accessibility score and recommendations |
|
| Get SEO analysis and recommendations |
|
⚡ Performance Tools
Tool | Description | Parameters |
| Get overall performance score |
|
| Get Core Web Vitals metrics |
|
| Compare performance across devices |
|
| Check against performance budgets |
|
| Find LCP optimization opportunities |
|
🔍 Analysis Tools
Tool | Description | Parameters |
| Find unused JavaScript code |
|
| Analyze all website resources |
|
🔒 Security Tools
Tool | Description | Parameters |
| Perform comprehensive security audit |
|
💬 Available Prompts
The Lighthouse MCP server includes reusable prompts that help LLMs provide structured analysis and recommendations:
📊 Analysis Prompts
Prompt | Description | Parameters |
| Analyze Lighthouse audit results |
|
| Compare before/after audit results |
|
| Get Core Web Vitals optimization recommendations |
|
| Get resource optimization recommendations |
|
📚 Available Resources
The Lighthouse MCP server provides built-in reference resources with essential guidelines and best practices:
Resource | Description | URI |
| Core Web Vitals performance thresholds |
|
| Performance optimization techniques and impact |
|
| WCAG 2.1 accessibility guidelines and issues |
|
| SEO best practices and optimization opportunities |
|
| Web security best practices and vulnerabilities |
|
| Performance budget recommendations by site type |
|
| Lighthouse audit categories and scoring methods |
|
| Framework-specific optimization guides |
|
🎯 Strategy Prompts
Prompt | Description | Parameters |
| Generate comprehensive performance improvement plan |
|
| Create custom performance budget recommendations |
|
| Generate SEO improvement recommendations |
|
| Create accessibility improvement guide |
|
🔧 Prompt Parameter Details
auditResults: JSON audit results from Lighthouse toolsfocusArea: Specific category to focus on ("performance","accessibility","seo","best-practices","agentic-browsing")beforeAudit/afterAudit: Lighthouse audit results before and after changeschangesImplemented: Description of changes made between auditscurrentMetrics: Current performance metrics from auditstargetGoals: Specific performance targets or business goalstimeframe: Timeline for implementing improvementsframework: Frontend framework or technology stackconstraints: Technical or business constraintswebsiteType: Type of website (e.g., e-commerce, blog, corporate)targetAudience: Target audience or market informationcomplianceLevel: WCAG compliance level ("AA"or"AAA")userGroups: Specific user groups to consider for accessibility
📋 Parameter Details
Common Parameters
url(required): The website URL to analyzedevice: Target device ("desktop"or"mobile", default:"desktop")includeDetails: Include detailed audit information (default:false)throttling: Enable network/CPU throttling (default:false)
Specific Parameters
categories: Lighthouse categories to audit (["performance", "accessibility", "best-practices", "seo", "agentic-browsing"])threshold: Custom thresholds for metrics (e.g.,{"lcp": 2.5, "inp": 200, "cls": 0.1})budget: Performance budget limits (e.g.,{"performanceScore": 90, "largestContentfulPaint": 2500})resourceTypes: Resource types to analyze (["images", "javascript", "css", "fonts", "other"])minBytes: Minimum file size threshold for analysis (default:2048)checks: Security checks to perform (["https", "csp", "hsts", "origin-isolation", "clickjacking", "trusted-types", "third-party-cookies", "deprecations"])
💡 Usage Examples
Basic Performance Audit
// Get overall performance score
{
"tool": "get_performance_score",
"arguments": {
"url": "https://example.com",
"device": "mobile"
}
}Core Web Vitals Analysis
// Check Core Web Vitals with custom thresholds
{
"tool": "get_core_web_vitals",
"arguments": {
"url": "https://example.com",
"device": "mobile",
"includeDetails": true,
"threshold": {
"lcp": 2.5,
"inp": 200,
"cls": 0.1
}
}
}Security Assessment
// Comprehensive security audit
{
"tool": "get_security_audit",
"arguments": {
"url": "https://example.com",
"checks": ["https", "csp", "hsts"]
}
}Resource Optimization
// Find optimization opportunities
{
"tool": "analyze_resources",
"arguments": {
"url": "https://example.com",
"resourceTypes": ["images", "javascript"],
"minSize": 1024
}
}Using Reference Resources
Access built-in guidelines and best practices:
// Get Core Web Vitals thresholds
{
"resource": {
"uri": "lighthouse://performance/core-web-vitals-thresholds"
}
}
// Access WCAG accessibility guidelines
{
"resource": {
"uri": "lighthouse://accessibility/wcag-guidelines"
}
}
// Get framework-specific optimization guides
{
"resource": {
"uri": "lighthouse://frameworks/optimization-guides"
}
}Using Prompts for Analysis
// Analyze audit results with focused recommendations
{
"prompt": "analyze-audit-results",
"arguments": {
"auditResults": "{...lighthouse audit json...}",
"focusArea": "performance"
}
}
// Create a performance improvement plan
{
"prompt": "create-performance-plan",
"arguments": {
"currentMetrics": "{...current performance metrics...}",
"targetGoals": "Achieve 90+ performance score and sub-2s LCP",
"timeframe": "3 months"
}
}
// Compare before/after audit results
{
"prompt": "compare-audits",
"arguments": {
"beforeAudit": "{...before audit results...}",
"afterAudit": "{...after audit results...}",
"changesImplemented": "Implemented lazy loading and image optimization"
}
}🎯 Use Cases
Performance Monitoring: Automated performance tracking and Core Web Vitals monitoring
Accessibility Compliance: WCAG 2.1 compliance checking and remediation guidance
SEO Optimization: Technical SEO audits and search engine optimization recommendations
Security Assessment: Vulnerability scanning and security best practice validation
Resource Optimization: Bundle analysis and optimization opportunity identification
Performance Budgets: Automated performance budget monitoring and alerting
CI/CD Integration: Automated quality gates and performance regression detection
🏗️ Architecture
The server is built using:
Model Context Protocol SDK: For MCP server implementation
Google Lighthouse: For web performance auditing
Chrome Launcher: For browser automation
TypeScript: For type safety and better developer experience
Zod: For runtime schema validation
🧪 Testing
npm run test:run # unit tests
npm run test:coverage # unit tests with coverage
npm run test:e2e # end-to-end testsThe end-to-end suite builds the server, launches it over stdio with a real MCP client, and
runs actual Lighthouse audits against a fixture page served on loopback. It requires Chrome
to be installed; set CHROME_PATH if it lives somewhere non-standard.
🤝 Contributing
Contributions are welcome! Please read our Contributing Guide for details on:
Code style and standards
Testing requirements
Pull request process
Development setup
📜 License
This project is licensed under the MIT License - see the LICENSE file for details.
🔒 Security
For security issues, please see our Security Policy.
📞 Support
🐛 Bug Reports: GitHub Issues
💬 Discussions: GitHub Discussions
📧 Email: security@codingrules.ai
🙏 Acknowledgments
Google Lighthouse team for the excellent auditing engine
Anthropic for the Model Context Protocol specification
The open source community for continuous inspiration and contributions
Built with ❤️ by Daniel Sogl
Available Tools
11 toolsanalyze_resourcesAnalyze Page ResourcesARead-only
Analyze website resources (images, JS, CSS, fonts) for optimization opportunities
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | URL to audit | |
| device | No | Device to emulate (default: desktop) | desktop |
| minSize | No | Minimum resource size in KB to include | |
| resourceTypes | No | Types of resources to analyze |
Output Schema
| Name | Required | Description |
|---|---|---|
| url | Yes | |
| device | Yes | |
| filters | Yes | |
| summary | Yes | |
| resources | Yes | |
| timestamp | Yes | |
| optimization | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true, so the agent knows this is a safe, read-only operation that may access external URLs. The description adds the focus on optimization opportunities but does not disclose details like whether it fetches live pages, handles redirects, or has rate limits. With annotations covering the safety profile, a 3 is appropriate – it adds some context but not rich behavioral detail.
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?
A single, clear sentence that front-loads the resource types and purpose. No waste, though it could be slightly more specific about the output or usage context. Efficient and to the point.
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 output schema exists and annotations cover safety, the description is adequate for a resource analysis tool. It doesn't explain return values (covered by output schema) or edge cases, but for a read-only analysis tool with 4 parameters, it is reasonably complete. Could benefit from mentioning that it analyzes a single URL or that it's for optimization, but these are minor gaps.
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 all four parameters are documented in the schema. The description adds the overall purpose but does not add syntax or format details beyond what the schema provides. Baseline 3 is correct when schema does the heavy lifting.
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 states a specific verb ('Analyze') and resource ('website resources') with a clear scope (images, JS, CSS, fonts) and purpose (optimization opportunities). It distinguishes from siblings like get_performance_score or get_seo_analysis by focusing on resource-level analysis, though it doesn't explicitly name a sibling it is not.
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 optimization analysis but does not explicitly state when to use this tool versus alternatives like find_unused_javascript or get_lcp_opportunities. The sibling list suggests related tools, but no exclusions or conditions are provided. The context is clear enough for a general audit, but lacks explicit routing guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
check_performance_budgetCheck Performance BudgetBRead-only
Check if website performance meets specified budget thresholds
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | URL to audit | |
| budget | Yes | ||
| device | No | Device to emulate (default: desktop) | desktop |
Output Schema
| Name | Required | Description |
|---|---|---|
| data | Yes | |
| summary | Yes | |
| recommendations | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint: true and openWorldHint: true, indicating a read-only operation that may access external resources. The description adds no extra behavioral context—it does not mention that the tool will perform a live audit, make network requests, or have rate limits. While there is no contradiction, the description fails to disclose the operation's networked nature, which is a meaningful behavioral trait 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 a single, efficient sentence with no filler. It conveys the purpose immediately and contains no redundant information. It earns a high score for being appropriately sized and front-loaded.
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's moderate complexity (nested budget object, optional device, output schema present), the description is minimal. It does not explain when to use it compared to siblings, nor does it clarify the semantics of the budget fields beyond what the schema provides. The output schema exists, so return values are covered, but the lack of usage guidance leaves the description somewhat incomplete for guiding an agent toward correct invocation in context.
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 67%: url and device have descriptions, but the budget object itself lacks a description at the top level. The inner properties are described, so the schema partially covers the main parameter. The description adds no additional meaning about how to structure the budget or interpret thresholds, leaving the agent to rely on the schema's inner property descriptions, which are adequate but not enriched.
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's function: checking website performance against budget thresholds. It uses a specific verb ('check') and resource ('website performance'), and the notion of 'budget thresholds' distinguishes it from sibling tools that return raw scores or metrics. However, it does not explicitly name an alternative or contrast with similar tools like get_performance_score, so it is clear but not strongly differentiated.
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?
There is no guidance on when to use this tool instead of siblings such as run_audit, get_performance_score, or get_core_web_vitals. The description only states what it does, leaving the agent to infer appropriate use. No exclusions, alternatives, or preconditions are provided, so usage context is entirely absent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
compare_mobile_desktopCompare Mobile vs DesktopARead-only
Compare website performance between mobile and desktop devices
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | URL to audit | |
| categories | No | ||
| throttling | No | Whether to throttle the audit (default: false) | |
| includeDetails | No | Include detailed metrics and recommendations |
Output Schema
| Name | Required | Description |
|---|---|---|
| data | Yes | |
| summary | Yes | |
| recommendations | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true, so the description does not need to restate safety. However, it adds no extra behavioral context such as how the comparison is structured, whether it returns a single report or dual reports, or any throttling implications. With annotations covering the read-only nature, the description provides marginal added value.
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?
A single, front-loaded sentence with zero filler. It delivers the core purpose immediately and does not elaborate unnecessarily.
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 is simple, annotations cover safety, and an output schema exists so return format details are not required. However, the description lacks usage guidance and does not mention any special considerations (e.g., throttling, category selection) that an agent might need to know for effective invocation. It is minimally sufficient but not comprehensive.
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 75% (high), and all parameters have descriptive names and default values in the schema. The description adds no parameter-specific meaning beyond what the schema already provides. The baseline 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 states a specific action (compare) on a defined resource (website performance) between mobile and desktop devices. It clearly distinguishes itself from sibling audit tools like run_audit or get_performance_score, which focus on single-device audits or individual metric categories.
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?
No explicit guidance is given on when to use this tool versus alternatives. It does not mention that it is the preferred tool when a mobile/desktop comparison is needed, nor does it exclude cases where a single-device audit would suffice. The usage context is only implied by the tool's name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
find_unused_javascriptFind Unused JavaScriptBRead-only
Find unused JavaScript code to reduce bundle size
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | URL to audit | |
| device | No | Device to emulate (default: desktop) | desktop |
| minBytes | No | Minimum unused bytes to report (default: 2048) |
Output Schema
| Name | Required | Description |
|---|---|---|
| url | Yes | |
| device | Yes | |
| summary | Yes | |
| timestamp | Yes | |
| unusedFiles | Yes | |
| thresholdBytes | No | |
| recommendations | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already carry readOnlyHint=true and openWorldHint=true, so the tool's non-destructive and open-world behavior is covered, and the description does not contradict the annotations. The description adds little beyond the generic 'find' behavior—no mention of how the scan is performed, what resources are fetched, or what output to expect—so it stays at the baseline for annotation-covered tools.
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 efficient sentence that immediately states the tool's core function. It avoids filler and front-loads the verb and resource, making it easy for an agent to parse at a glance.
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 is simple, and the schema, annotations, and output schema fill in a lot. However, the description does not mention the need for a URL or clarify how this audit relates to the many other audit siblings, leaving the agent without enough context to confidently choose and invoke it among the broader toolset.
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 coverage of the input parameters is 100%, so the baseline is 3. The description mentions bundle size, which loosely relates to minBytes, but it does not add detail about how url, device, or minBytes interplay; the schema carries the burden.
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 ('Find') and a concrete resource ('unused JavaScript code'), and adds the motivating goal of reducing bundle size. It is clear enough to be distinguished from performance scoring tools, but it does not explicitly differentiate it from a sibling like analyze_resources, so it misses the 5 criterion.
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?
No guidance is given on when to use this tool versus alternatives such as get_performance_score, analyze_resources, or run_audit. There are no conditions, prerequisites, or exclusion criteria—only a one-line purpose statement.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_accessibility_scoreGet Accessibility ScoreBRead-only
Get the accessibility score and recommendations for a website
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | URL to audit | |
| device | No | Device to emulate (default: desktop) | desktop |
| includeDetails | No | Include detailed metrics and recommendations |
Output Schema
| Name | Required | Description |
|---|---|---|
| data | Yes | |
| summary | Yes | |
| recommendations | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description only restates the purpose and adds no behavioral traits beyond the annotations. Annotations declare readOnlyHint and openWorldHint, but the description does not disclose what an audit entails (e.g., live network request, duration, caching). Since annotations are present, the burden is lower, but the description adds no value.
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?
A single, clear sentence with no redundancy. The primary action and outcome are front-loaded. Perfectly concise.
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 is simple, has an output schema (though not shown), and full schema coverage. The description is adequate but omits context about how the audit is performed (live vs. cached) and what 'recommendations' entail. Given the complexity level, this is a minimum-viable description.
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% for all parameters, including device enum and includeDetails. The description does not elaborate on parameters, but the schema carries the full burden. 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 'Get' and the resource 'accessibility score and recommendations for a website'. It distinguishes itself from performance, SEO, and security siblings by naming the accessibility domain, but does not explicitly reference alternatives.
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?
No guidance is given on when to use this tool versus alternatives. It does not mention prerequisites, use cases, or exclusions. An agent would have to infer from the name that it is for accessibility audits only.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_core_web_vitalsGet Core Web VitalsARead-only
Get Core Web Vitals metrics for a website
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | URL to audit | |
| device | No | Device to emulate (default: desktop) | desktop |
| threshold | No | ||
| includeDetails | No | Include detailed metrics and recommendations |
Output Schema
| Name | Required | Description |
|---|---|---|
| data | Yes | |
| summary | Yes | |
| recommendations | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate readOnlyHint=true and openWorldHint=true, meaning the tool performs a safe, read-only operation. The description adds no new behavioral information beyond the name, but given the annotations already cover the safety and non-mutating nature, the description's transparency is adequate. It does not contradict annotations, and the openWorldHint suggests the tool may reach external websites, which is not disclosed in the description but is implied by the term 'website'. The absence of details about rate limits or external dependencies is minor since the tool is read-only.
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, concise sentence that directly states the tool's purpose. Every word is meaningful; there is no fluff or repetition. It is appropriately front-loaded, with the verb 'Get' immediately clarifying the action. 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 the tool's complexity (4 parameters, nested threshold object, output schema present), the description is relatively sparse, but the robust input schema and presence of an output schema reduce the need for extensive description. The description covers the 'what' but not the 'how' or nuances like how thresholds affect evaluation or what 'detailed metrics' entails. With the output schema handling return values and the schema handling parameters, the description is sufficient for a read-only tool, so it slightly exceeds the minimum viable.
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 describes all parameters with reasonable detail (url, device, threshold object with fields, includeDetails). The schema description coverage is 75%, so most parameters are documented. The description does not add any additional meaning beyond what the schema provides; for example, it doesn't explain how thresholds are used or how includeDetails alters the output. With high coverage, the baseline is 3, and the description contributes minimal value.
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 'Get Core Web Vitals metrics for a website' clearly identifies the specific metrics (Core Web Vitals) and the resource (a website), using a precise verb. It is specific enough to distinguish it from generic measures like 'get_performance_score', though it could name a specific competitor (e.g., get_performance_score) to further differentiate. The title and description are aligned, with the description adding 'metrics' to clarify the resource.
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 gives no explicit guidance on when to use this tool versus siblings such as 'get_performance_score' or 'compare_mobile_desktop'. However, the purpose is clear enough that an agent can infer it is for Core Web Vitals (LCP, INP, CLS) specifically, which is a subset of performance. There are no exclusions or alternatives mentioned, so the usage context is implied rather than explicit, warranting a mid-range score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_lcp_opportunitiesGet LCP OpportunitiesBRead-only
Get LCP optimization opportunities for a website
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | URL to audit | |
| device | No | Device to emulate (default: desktop) | desktop |
| threshold | No | LCP threshold in seconds (default: 2.5) | |
| includeDetails | No | Include detailed metrics and recommendations |
Output Schema
| Name | Required | Description |
|---|---|---|
| data | Yes | |
| summary | Yes | |
| recommendations | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is covered. The description itself adds little behavioral context, such as whether the tool performs a live network audit or how results are ordered, but it does not contradict 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 a single front-loaded sentence with no filler or repetition. It states the essential purpose efficiently, and nothing extraneous is included.
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?
Output schema and complete parameter documentation reduce the need for the description to explain return values or parameters. However, the lack of usage guidance relative to sibling tools and the absence of operational caveats leave the overall context only adequate, not fully 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%, and each parameter is documented with types, defaults, enums, and constraints. The description adds no additional parameter-level meaning beyond what the schema already provides, so the baseline 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 identifies the verb ('Get') and resource ('LCP optimization opportunities for a website'), so an agent understands the tool's core purpose. It does not explicitly distinguish itself from sibling tools such as get_core_web_vitals or get_performance_score, but the LCP-specific focus provides reasonable differentiation.
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?
No guidance is provided about when to use this tool versus alternatives like get_core_web_vitals, get_performance_score, or run_audit. There are no stated exclusions, prerequisites, or context cues, leaving the agent to infer selection based solely on the tool name and description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_performance_scoreGet Performance ScoreCRead-only
Get the performance score for a website
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | URL to audit | |
| device | No | Device to emulate (default: desktop) | desktop |
Output Schema
| Name | Required | Description |
|---|---|---|
| data | Yes | |
| summary | Yes | |
| recommendations | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds no behavioral information beyond the annotations. It does not state that the tool fetches a live URL, runs a Lighthouse audit, or that it may take time or require network access. With readOnlyHint and openWorldHint already provided, the description contributes nothing extra.
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 efficient sentence with no wasted words. It is appropriately concise for the tool's simplicity, though it is not front-loaded with any additional context because there is none.
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 simple two-parameter tool with an output schema, the description is minimally adequate but lacks key contextual details such as what constitutes a 'performance score' (e.g., Lighthouse metric), prerequisites like URL accessibility, or any potential variability. Given the existence of siblings, it is not sufficiently complete to guide correct selection.
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 coverage is 100% as both 'url' and 'device' are described in the input schema. The description adds no additional meaning about the parameters or their usage, so the baseline 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 uses a specific verb ('get') and resource ('performance score for a website'), making the intent clear. However, it does not differentiate from nearby siblings like get_core_web_vitals or get_lcp_opportunities, which also relate to performance metrics, so there is a mild ambiguity.
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?
There is no guidance on when to use this tool versus alternatives such as get_core_web_vitals or analyze_resources. No exclusions or recommended contexts are provided, leaving an agent to infer the appropriate choice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_security_auditGet Security AuditARead-only
Perform security audit checking HTTPS, CSP, and other security measures
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | URL to audit | |
| checks | No | Specific security checks to perform | |
| device | No | Device to emulate (default: desktop) | desktop |
Output Schema
| Name | Required | Description |
|---|---|---|
| data | Yes | |
| summary | Yes | |
| recommendations | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is covered; the description adds that it checks HTTPS/CSP and 'other security measures', which is useful but thin. It does not mention network behavior toward the target URL or response characteristics, though openWorldHint partially implies external access. No contradiction 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?
A single sentence that front-loads the action ('Perform security audit') and gives concrete examples of check types. The tail 'and other security measures' is slightly vague but functions as a pointer to the schema's enum without bloating the text.
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?
With an output schema present, full parameter documentation in the schema, and annotations covering the read-only/open-world safety profile, the description is largely sufficient for correct invocation. The notable gap is the missing relationship to 'run_audit', which is the one sibling that could genuinely confuse tool selection.
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% — url, checks, and device are all documented in the input schema, so the description carries no required burden. The mention of 'HTTPS, CSP' marginally reinforces the enum values in the 'checks' parameter, but adds little beyond the schema's own descriptions.
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 names a specific verb+resource ('security audit') and concrete focus areas (HTTPS, CSP), which cleanly separates it from the nine accessibility/SEO/performance siblings. However, it does not distinguish itself from the overlapping sibling 'run_audit', whose scope is left undefined, so an agent cannot fully tell them apart.
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?
Usage is implied by the security focus and the 'checking HTTPS, CSP' phrasing, which suggests this is the tool for security-related audits. There is no explicit statement of when to prefer it over 'run_audit' or when not to use it, leaving the selection logic to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_seo_analysisGet SEO AnalysisCRead-only
Get SEO analysis and recommendations for a website
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | URL to audit | |
| device | No | Device to emulate (default: desktop) | desktop |
| includeDetails | No | Include detailed metrics and recommendations |
Output Schema
| Name | Required | Description |
|---|---|---|
| data | Yes | |
| summary | Yes | |
| recommendations | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnlyHint and openWorldHint, so the safety profile is covered. The description adds no further behavioral context such as rate limits, time expectations, or the nature of external fetches, offering minimal value 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 a single, front-loaded sentence that efficiently states the core purpose. It is appropriately concise for a tool whose parameters and return format are fully specified in the schema.
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 large set of specialized audit siblings, the description is thin on context for selection. It doesn't mention that this provides a comprehensive overview or when to prefer it over targeted tools, and it omits any note about output shape (though the output schema covers that). The lack of usage guidance undermines 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?
The schema provides full descriptions for all three parameters at 100% coverage. The description adds no extra meaning, so the baseline of 3 applies. Nothing is lacking in terms of parameter documentation.
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 retrieves SEO analysis and recommendations for a website, using a specific verb and resource. While it doesn't explicitly contrast with sibling audit tools, the name and resource make it distinct from performance, accessibility, and security-specific tools.
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 provides no guidance on when to choose this tool over alternative audit or metric tools. An agent must infer from the name that it covers general SEO, with no explicit exclusions or references to siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
run_auditRun Lighthouse AuditBRead-only
Run a comprehensive Lighthouse audit on a website
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | URL to audit | |
| device | No | Device to emulate (default: desktop) | desktop |
| categories | No | ||
| throttling | No | Whether to throttle the audit (default: false) |
Output Schema
| Name | Required | Description |
|---|---|---|
| data | Yes | |
| summary | Yes | |
| recommendations | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true and openWorldHint=true, so the agent knows this is a safe read operation. The description adds 'comprehensive' but doesn't disclose behavioral traits like the fact that it runs multiple categories, the time it takes, or that it may be slower than targeted audits. With annotations covering safety, the description adds minimal behavioral context beyond the word 'comprehensive'.
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 with no waste. It's front-loaded with the verb and resource. It could be slightly more informative, but it's concise and to the point.
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 an output schema (not shown) and 4 parameters with 75% schema coverage. The description is minimal but the schema and annotations carry some weight. However, given the complexity of a Lighthouse audit (multiple categories, device emulation, throttling), the description could explain what 'comprehensive' means and how it relates to the sibling tools. It's adequate but not 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 75%, so most parameters are documented in the schema. The description doesn't add any parameter-specific meaning beyond what the schema provides. The 'categories' parameter has an enum with 'agentic-browsing' which is unusual, but the description doesn't explain it. Baseline 3 is appropriate since the schema does most of the work.
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 states a specific verb ('Run') and resource ('Lighthouse audit'), and the title clarifies it's a website audit. It's clear what the tool does, though it doesn't explicitly differentiate from siblings like get_performance_score or get_accessibility_score, which are more specific. The description is broad but not misleading.
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 running a comprehensive audit, but it doesn't explicitly state when to use this tool versus the more specific sibling tools (e.g., get_performance_score, get_accessibility_score). It doesn't mention alternatives or exclusions. The context signals show many sibling tools that are more targeted, so the description should guide the agent on when to choose this comprehensive audit over those.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
TDQS
There is significant overlap between run_audit and the specific getter tools (accessibility, SEO, performance), as run_audit likely encompasses all of those. Additionally, get_lcp_opportunities, find_unused_javascript, and analyze_resources all target performance optimization with unclear boundaries.
Most tools follow a clear verb_noun pattern (e.g., get_*, run_audit, compare_mobile_desktop, check_performance_budget). Minor deviations include run_audit vs. get_security_audit (both use 'audit' but different verbs) and find_unused_javascript/analyze_resources not using 'get_' prefix, but the overall convention is consistent.
With 11 tools, the set is well-scoped for a website auditing server. Each tool covers a distinct aspect of Lighthouse auditing (performance, SEO, accessibility, security, resources), and the count is within the ideal 3-15 range without feeling bloated.
The tool surface covers major Lighthouse categories (performance, accessibility, SEO, security, Core Web Vitals, resources) and includes useful extras like budget checks and mobile/desktop comparison. However, it lacks explicit best practices and PWA audits, which are standard Lighthouse categories, creating minor gaps.
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