{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-browserstack","slug":"browserstack","name":"BrowserStack","type":"mcp","url":"https://github.com/browserstack/mcp-server","page_url":"https://unfragile.ai/browserstack","categories":["mcp-servers","testing-quality"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-browserstack__cap_0","uri":"capability://tool.use.integration.mcp.based.tool.registration.and.protocol.bridging.for.testing.platforms","name":"mcp-based tool registration and protocol bridging for testing platforms","description":"Implements the Model Context Protocol (MCP) standard using @modelcontextprotocol/sdk to expose BrowserStack testing capabilities as callable tools to AI clients. The server uses stdin/stdout transport to communicate with AI IDEs (VSCode, Cursor, Claude Desktop), automatically registering 20+ tools across 7 functional categories with Zod-based schema validation for parameter types. Each tool follows a consistent pattern: input validation → authentication via environment variables → Axios-based HTTP API calls to BrowserStack services → structured response formatting with error handling.","intents":["I want to call BrowserStack testing functions directly from my AI IDE without leaving the editor","I need my AI agent to programmatically trigger test runs and retrieve results through a standardized protocol","I want to integrate BrowserStack capabilities into Claude, VSCode Copilot, or Cursor without custom API wrappers"],"best_for":["AI IDE users (VSCode with Copilot, Cursor, Claude Desktop) who need native BrowserStack integration","Teams building AI agents that require standardized tool calling interfaces","Developers migrating from REST API calls to MCP-based tool orchestration"],"limitations":["Requires MCP-compatible AI client — not all IDEs support MCP protocol yet","Tool execution latency depends on BrowserStack API response times (typically 500ms-5s per call)","No built-in request queuing or rate limiting — relies on BrowserStack account limits","Stateless server design means no persistence of test context between tool calls without external storage"],"requires":["Node.js 18+","BrowserStack API credentials (BROWSERSTACK_USERNAME and BROWSERSTACK_ACCESS_KEY environment variables)","MCP-compatible AI client (VSCode 1.80+, Cursor, Claude Desktop, or equivalent)","@modelcontextprotocol/sdk package (included in npm distribution)"],"input_types":["JSON-structured tool parameters validated against Zod schemas","Environment variables for authentication","Configuration files (.vscode/mcp.json, .cursor/mcp.json, etc.)"],"output_types":["Structured JSON responses from BrowserStack APIs","Error objects with descriptive messages","Tool execution logs via Pino logger"],"categories":["tool-use-integration","mcp-protocol"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-browserstack__cap_1","uri":"capability://automation.workflow.live.interactive.browser.and.mobile.app.testing.sessions.with.real.time.control","name":"live interactive browser and mobile app testing sessions with real-time control","description":"Enables AI agents and developers to launch interactive testing sessions on real BrowserStack devices through tools like runBrowserLiveSession and runAppLiveSession. The implementation manages device allocation, session lifecycle, and real-time interaction by calling BrowserStack's Live Testing API, returning session URLs and device metadata that allow users to control browsers/apps in real-time. Sessions are authenticated via BrowserStack credentials and support both web browsers and native mobile applications across iOS and Android platforms.","intents":["I want to start an interactive testing session on a specific device without manually navigating BrowserStack's UI","I need my AI agent to provision a live testing environment and return a URL I can immediately use","I want to test my app on real iOS/Android devices through an AI-driven workflow"],"best_for":["QA engineers and developers who need rapid device provisioning for manual testing","AI agents that need to spawn interactive environments for human-in-the-loop testing","Teams testing mobile apps across multiple device configurations"],"limitations":["Live sessions consume BrowserStack concurrency limits — only N simultaneous sessions allowed per account tier","Session URLs are temporary and expire after inactivity (typically 30 minutes)","No programmatic control of browser/app actions — sessions are for human interaction only","Device availability depends on BrowserStack's real device pool — some devices may have queue times"],"requires":["BrowserStack Live Testing subscription (included in most plans)","Valid BrowserStack API credentials","Network access to BrowserStack's session infrastructure","Browser or mobile app to test"],"input_types":["Device specifications (browser type, OS version, device name)","Optional: project name, build identifier","Optional: custom capabilities (screen resolution, locale, etc.)"],"output_types":["Session URL (clickable link to live testing interface)","Device metadata (device name, OS, browser version)","Session ID for session management"],"categories":["automation-workflow","testing-platform"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-browserstack__cap_10","uri":"capability://safety.moderation.authentication.and.credential.management.via.environment.variables","name":"authentication and credential management via environment variables","description":"Implements credential management using environment variables (BROWSERSTACK_USERNAME and BROWSERSTACK_ACCESS_KEY) for secure storage of BrowserStack API credentials. The system validates credentials at server startup and injects them into all API requests via Basic Auth headers. Credentials are never logged or exposed in error messages, and the system fails fast if credentials are missing or invalid.","intents":["I want to securely provide BrowserStack credentials to the MCP server without hardcoding them","I need the server to validate credentials at startup and fail fast if they're invalid","I want to rotate credentials without restarting the server"],"best_for":["DevOps teams deploying MCP server in containerized environments","CI/CD pipelines that need to inject credentials via environment variables","Teams with strict credential management policies"],"limitations":["Credentials are loaded at startup — rotation requires server restart","Environment variables are visible in process listings — requires OS-level security","No credential expiration or automatic rotation — manual management required","No support for credential files or secret managers (Vault, AWS Secrets Manager, etc.)"],"requires":["BROWSERSTACK_USERNAME environment variable set","BROWSERSTACK_ACCESS_KEY environment variable set","Valid BrowserStack account with API access"],"input_types":["Environment variables (BROWSERSTACK_USERNAME, BROWSERSTACK_ACCESS_KEY)"],"output_types":["Validation status (success or error)","Error messages if credentials are invalid or missing"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-browserstack__cap_11","uri":"capability://safety.moderation.zod.based.parameter.validation.for.tool.inputs.with.schema.enforcement","name":"zod-based parameter validation for tool inputs with schema enforcement","description":"Implements input validation using Zod schemas for all tool parameters, ensuring type safety and catching invalid inputs before API calls. Each tool defines a Zod schema that validates parameter types, required fields, string formats (URLs, email addresses), enum values, and numeric ranges. Validation errors are caught and returned to the client with detailed error messages indicating which fields are invalid and why.","intents":["I want tool calls to fail fast with clear error messages if parameters are invalid","I need type safety for tool parameters without manual validation code","I want to prevent invalid API calls that would waste BrowserStack API quota"],"best_for":["MCP server developers extending tool functionality","Teams building AI agents that need to validate tool parameters before execution","Operators monitoring tool call quality and error rates"],"limitations":["Validation adds ~1-5ms overhead per tool call","Schema definitions must be maintained manually — no automatic schema generation from BrowserStack API specs","Complex validation logic (cross-field validation, conditional requirements) requires custom Zod refinements","Error messages are technical — may require translation for non-technical users"],"requires":["Zod library (included in npm distribution)","Zod schema definitions for each tool"],"input_types":["Tool parameters as JSON objects"],"output_types":["Validation success or error objects","Detailed error messages with field names and validation rules"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-browserstack__cap_12","uri":"capability://tool.use.integration.multi.client.deployment.configuration.for.vscode.cursor.and.claude.desktop","name":"multi-client deployment configuration for vscode, cursor, and claude desktop","description":"Supports deployment across multiple AI clients (VSCode with Copilot, Cursor IDE, Claude Desktop) through client-specific configuration files (.vscode/mcp.json, .cursor/mcp.json, ~/claude_desktop_config.json). The MCP server is distributed as an npm package and can be installed via npx with environment variables, with each client reading its configuration file to discover and connect to the server via stdin/stdout transport. Configuration includes server command, environment variables, and tool availability settings.","intents":["I want to use BrowserStack tools in my preferred AI IDE (VSCode, Cursor, or Claude Desktop)","I need to configure the MCP server once and have it work across multiple clients","I want to deploy the MCP server in a containerized environment for team use"],"best_for":["Teams using multiple AI IDEs and needing consistent BrowserStack integration","DevOps teams deploying MCP server in shared environments","Individual developers using different AI tools for different tasks"],"limitations":["Each client requires separate configuration file — no centralized configuration","Configuration changes require client restart — no hot reload","Client-specific features may not be supported across all platforms (e.g., VSCode extensions vs. Cursor)","npm installation required for each deployment — no pre-built binaries"],"requires":["Node.js 18+ installed and in PATH","npm or npx available","Client-specific configuration file created in correct location","BrowserStack API credentials in environment variables"],"input_types":["Client configuration files (JSON)","Environment variables (BROWSERSTACK_USERNAME, BROWSERSTACK_ACCESS_KEY)","npm package specification (browserstack/mcp-server)"],"output_types":["MCP server process running on stdin/stdout","Tool availability in AI client UI","Connection status and error messages"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-browserstack__cap_13","uri":"capability://tool.use.integration.modular.tool.organization.across.7.functional.categories.with.consistent.patterns","name":"modular tool organization across 7 functional categories with consistent patterns","description":"Organizes 20+ tools into 7 functional categories (SDK Integration, Live Testing, Test Management, Automation, Accessibility, Observability, AI Agent Tools) with each category following a consistent implementation pattern: input validation via Zod schemas, authentication via environment variables, API calls via shared Axios client, response formatting, and error handling. This modular architecture enables easy tool addition and maintenance while ensuring consistent behavior across all tools.","intents":["I want to understand the full scope of BrowserStack capabilities available through MCP","I need to find the right tool for my testing workflow without reading documentation","I want to extend the MCP server with new tools following established patterns"],"best_for":["MCP server developers extending tool functionality","Teams evaluating BrowserStack capabilities through MCP","Operators managing tool availability and access control"],"limitations":["Tool organization is fixed — cannot customize tool grouping per deployment","All tools are exposed by default — no per-tool access control","Tool discovery is manual — no automatic tool documentation generation","Tool interdependencies are implicit — no dependency management between tools"],"requires":["Understanding of BrowserStack service categories","Familiarity with MCP tool registration patterns"],"input_types":["Tool category specification","Tool implementation code following established patterns"],"output_types":["Registered tools available in AI client UI","Tool documentation and parameter schemas"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-browserstack__cap_14","uri":"capability://automation.workflow.asynchronous.test.execution.with.polling.and.webhook.support.for.result.retrieval","name":"asynchronous test execution with polling and webhook support for result retrieval","description":"Handles asynchronous test execution patterns where test runs are queued and executed in the background, with results retrieved via polling or webhook callbacks. The implementation supports both synchronous tool calls (which return immediately with a test run ID) and asynchronous result retrieval (which polls BrowserStack's API or waits for webhook notifications). This enables long-running tests to execute without blocking the AI client.","intents":["I want to trigger a test run and continue working while it executes in the background","I need to retrieve test results after they complete without polling manually","I want my AI agent to wait for test completion and then analyze results automatically"],"best_for":["CI/CD pipelines running long-duration test suites","AI agents that need to trigger tests and wait for results asynchronously","Teams testing on slow or congested device pools"],"limitations":["Polling adds latency — results are not immediately available (typically 30-60 second delay)","Webhook support requires publicly accessible callback URL — not suitable for localhost development","No built-in timeout handling — long-running tests may exceed client timeouts","Polling can consume API quota — high-frequency polling may hit rate limits"],"requires":["Test run ID from initial tool call","Polling interval configuration or webhook endpoint","Network access to BrowserStack's result APIs"],"input_types":["Test run ID","Optional: polling interval (seconds)","Optional: webhook URL for callbacks"],"output_types":["Test run status (queued, running, completed)","Test results (pass/fail, failure details, screenshots)","Webhook callback with result data"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-browserstack__cap_2","uri":"capability://automation.workflow.automated.test.case.creation.and.test.run.management.with.structured.metadata","name":"automated test case creation and test run management with structured metadata","description":"Provides tools (createTestCase, createTestRun, listTestRuns) that allow AI agents to programmatically create test cases with structured metadata, execute test runs, and retrieve test execution history. The implementation uses Axios HTTP clients to call BrowserStack's Test Management API, accepting test case definitions (name, description, steps, expected results) and test run parameters (device configurations, build identifiers), then returning test IDs and run status. Test cases are stored in BrowserStack's backend and can be reused across multiple test runs.","intents":["I want to create test cases programmatically from AI-generated test specifications","I need to trigger automated test runs on specific device configurations and retrieve results","I want to maintain a history of test executions and link them to builds or releases"],"best_for":["QA teams automating test case generation from requirements or user stories","CI/CD pipelines that need to programmatically create and execute test runs","AI agents that generate test cases and need to execute them immediately"],"limitations":["Test case creation requires well-structured input — malformed test steps may cause API validation errors","Test run execution is asynchronous — results are not immediately available; polling or webhooks required","No built-in test result analysis — raw test data returned without automatic failure categorization","Test case storage is account-scoped — no fine-grained access control per team or project"],"requires":["BrowserStack Test Management subscription","Valid BrowserStack API credentials","Structured test case definitions (name, steps, expected results)","Target device/browser configurations"],"input_types":["Test case metadata (name, description, test steps as structured objects)","Device configurations (browser type, OS, device name)","Build identifiers for linking test runs to releases"],"output_types":["Test case IDs (for future reference and reuse)","Test run IDs and status (queued, running, completed)","Test execution history with timestamps"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-browserstack__cap_3","uri":"capability://image.visual.automated.screenshot.capture.and.visual.regression.detection.across.devices","name":"automated screenshot capture and visual regression detection across devices","description":"Exposes tools (takeAppScreenshot, fetchAutomationScreenshots) that capture screenshots from automated test runs or live sessions across multiple devices and browsers. The implementation calls BrowserStack's App Automate and Automation APIs to retrieve screenshot artifacts, supporting both native mobile apps and web browsers. Screenshots are returned as image data or URLs and can be used for visual regression testing, documentation, or failure analysis without requiring manual screenshot collection.","intents":["I want to capture screenshots from test runs across 10+ device configurations automatically","I need to retrieve screenshots from failed tests for debugging without accessing BrowserStack UI","I want to generate visual documentation of my app across different devices programmatically"],"best_for":["QA teams performing visual regression testing across device matrices","CI/CD pipelines that need automated screenshot collection for test reports","AI agents analyzing test failures through visual inspection"],"limitations":["Screenshots are only available for completed test runs — cannot capture from live sessions","Image data can be large (1-5MB per screenshot) — bulk retrieval may require pagination","No built-in visual comparison — requires external tools (Pixelmatch, Percy, etc.) for regression detection","Screenshot availability depends on test run completion — asynchronous retrieval required"],"requires":["Completed test runs with screenshot capture enabled","BrowserStack App Automate or Automation subscription","Valid BrowserStack API credentials","Network bandwidth for image transfer"],"input_types":["Test run IDs or automation session IDs","Optional: screenshot filters (device type, test step, etc.)"],"output_types":["Image data (PNG/JPEG) or image URLs","Screenshot metadata (device, browser version, timestamp)","Thumbnail URLs for quick preview"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-browserstack__cap_4","uri":"capability://safety.moderation.wcag.ada.accessibility.compliance.scanning.with.automated.issue.detection","name":"wcag/ada accessibility compliance scanning with automated issue detection","description":"Implements the startAccessibilityScan tool that triggers automated accessibility audits on web applications using BrowserStack's Accessibility Testing service. The tool calls the Accessibility API with target URLs and optional scan parameters, returning a list of detected accessibility violations (WCAG 2.1 Level A/AA/AAA, ADA compliance issues) with severity levels and remediation suggestions. Scans run asynchronously and results are retrieved via polling or webhook integration.","intents":["I want to scan my web app for WCAG compliance issues without manual accessibility testing","I need to generate accessibility audit reports for compliance documentation","I want my AI agent to identify and prioritize accessibility fixes based on severity"],"best_for":["Development teams building accessible web applications","Compliance officers needing automated accessibility audit trails","AI agents that need to identify accessibility issues for automated remediation"],"limitations":["Scans only web applications — no native mobile app accessibility testing","Automated scanning detects ~60-70% of accessibility issues; manual review still required for complex interactions","Scan results depend on page complexity — dynamic content may require custom scan configuration","No automatic remediation — results are detection and reporting only"],"requires":["BrowserStack Accessibility Testing subscription","Valid BrowserStack API credentials","Publicly accessible web application URL or staging environment","WCAG compliance standard specification (A, AA, or AAA)"],"input_types":["Target URL to scan","Optional: WCAG compliance level (A, AA, AAA)","Optional: custom scan parameters (viewport size, wait time, etc.)"],"output_types":["List of accessibility violations with WCAG criteria references","Severity levels (critical, major, minor)","Remediation suggestions and code examples","Scan report URL for detailed analysis"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-browserstack__cap_5","uri":"capability://planning.reasoning.test.failure.analysis.and.self.healing.selector.detection.for.flaky.tests","name":"test failure analysis and self-healing selector detection for flaky tests","description":"Provides tools (getFailuresInLastRun, fetchSelfHealedSelectors) that analyze test failures and detect automatically-healed selectors in failed test runs. The implementation queries BrowserStack's Observability API to retrieve failure details (assertion errors, timeout errors, element not found), and identifies selectors that were automatically corrected by BrowserStack's self-healing engine. This enables developers to understand why tests failed and which selectors need manual updates.","intents":["I want to understand why my automated tests failed without manually reviewing logs","I need to identify which CSS/XPath selectors are brittle and need updating","I want my AI agent to suggest selector fixes based on self-healing detection"],"best_for":["QA engineers debugging flaky test failures","Teams using BrowserStack's self-healing feature to reduce test maintenance","AI agents analyzing test failures to suggest code fixes"],"limitations":["Self-healing detection only works if self-healing feature is enabled in test configuration","Failure analysis is post-execution only — cannot predict failures before they occur","No automatic selector replacement — results are detection and reporting only","Limited to BrowserStack's self-healing engine capabilities — some selector issues may not be detected"],"requires":["Completed test runs with failure data","BrowserStack Automation or App Automate subscription","Valid BrowserStack API credentials","Self-healing feature enabled in test configuration (for selector detection)"],"input_types":["Test run IDs","Optional: failure type filters (assertion, timeout, element not found, etc.)"],"output_types":["Failure details (error message, assertion, stack trace)","Failure type classification","Self-healed selectors with original and corrected values","Failure timestamp and affected test step"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-browserstack__cap_6","uri":"capability://data.processing.analysis.device.and.browser.capability.discovery.with.version.caching.and.availability.tracking","name":"device and browser capability discovery with version caching and availability tracking","description":"Implements device cache and version management infrastructure that maintains an up-to-date inventory of available devices, browsers, and OS versions supported by BrowserStack. The system periodically queries BrowserStack's Device API and caches results locally to reduce API calls, exposing device capabilities (screen resolution, RAM, CPU, supported features) through tool parameters. The cache is invalidated on a configurable schedule and supports filtering by device type, OS, browser, and custom capabilities.","intents":["I want to know what devices and browsers are available before creating a test run","I need to filter devices by specific capabilities (e.g., devices with 5G support)","I want my AI agent to suggest optimal device configurations for testing based on availability"],"best_for":["QA teams managing large device matrices and needing capability discovery","AI agents that need to recommend device configurations based on app requirements","CI/CD pipelines that need to validate device availability before test execution"],"limitations":["Cache invalidation is time-based — real-time device availability not guaranteed","Device list can be large (1000+ devices) — filtering and pagination required for performance","Capability data is static — dynamic device state (current load, queue time) not included","Cache storage is in-memory — lost on server restart without persistent backend"],"requires":["BrowserStack API credentials","Network access to BrowserStack Device API","Sufficient memory for device cache (typically <50MB for full device list)"],"input_types":["Device filter criteria (device type, OS, browser, capabilities)","Optional: cache invalidation signal"],"output_types":["List of available devices with metadata (name, OS, browser version, screen resolution)","Device capabilities (RAM, CPU, supported features, 5G, etc.)","Cache timestamp and next refresh time"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-browserstack__cap_7","uri":"capability://automation.workflow.local.tunnel.management.for.testing.private.internal.applications","name":"local tunnel management for testing private/internal applications","description":"Manages BrowserStack Local Tunnel connections that enable testing of private, internal, or localhost applications without exposing them to the internet. The implementation handles tunnel lifecycle (creation, authentication, health checks, teardown) using BrowserStack's Local API, supporting both direct tunnel connections and proxy-based routing. Tunnels are authenticated via BrowserStack credentials and can be shared across multiple test runs within a session.","intents":["I want to test my localhost development server on BrowserStack devices without deploying to staging","I need to test internal applications behind corporate firewalls on real devices","I want my CI/CD pipeline to automatically create and destroy tunnels for test isolation"],"best_for":["Development teams testing local applications on real devices","CI/CD pipelines that need secure testing of internal applications","Teams with strict security requirements preventing public staging deployments"],"limitations":["Tunnel creation adds 10-30 seconds overhead per test run","Bandwidth is limited by tunnel connection — high-traffic testing may experience slowdowns","Tunnels are account-scoped — concurrent tunnel limits depend on BrowserStack plan","Tunnel stability depends on network conditions — disconnections may cause test failures"],"requires":["BrowserStack Local subscription (included in most plans)","Valid BrowserStack API credentials","Network access to BrowserStack's tunnel infrastructure","Local application running and accessible on localhost or internal network"],"input_types":["Local application URL (localhost:port or internal IP)","Optional: tunnel configuration (proxy settings, SSL verification, etc.)","Optional: tunnel identifier for reuse"],"output_types":["Tunnel ID and connection status","Tunnel URL for use in test configurations","Health check status and latency metrics"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-browserstack__cap_8","uri":"capability://safety.moderation.structured.error.handling.and.instrumentation.with.pino.based.logging","name":"structured error handling and instrumentation with pino-based logging","description":"Implements comprehensive error handling and observability infrastructure using Pino logger for structured logging of all tool executions, API calls, and failures. The system captures error context (request parameters, API responses, stack traces), categorizes errors by type (validation, authentication, rate limiting, API errors), and provides detailed error messages to clients. Logs are structured as JSON for easy parsing and integration with observability platforms (ELK, Datadog, etc.).","intents":["I want detailed logs of all BrowserStack API calls for debugging integration issues","I need to understand why a tool call failed with specific error context","I want to monitor MCP server health and performance through structured logs"],"best_for":["DevOps teams monitoring MCP server health and performance","Developers debugging integration issues between AI clients and BrowserStack","Teams using centralized logging platforms (ELK, Datadog, CloudWatch)"],"limitations":["Structured logging adds ~5-10ms overhead per tool call","Log volume can be high in production — requires log rotation and retention policies","Sensitive data (API keys, credentials) must be redacted manually in log configuration","Log parsing requires JSON-aware tools — plain text logs not human-readable"],"requires":["Pino logger (included in npm distribution)","Log output destination (stdout, file, or external service)","Optional: log aggregation platform (ELK, Datadog, etc.)"],"input_types":["Tool execution events (name, parameters, start time)","API call details (method, URL, headers, response code)","Error events (type, message, stack trace)"],"output_types":["Structured JSON logs with timestamp, level, context","Error objects with categorization and remediation hints","Performance metrics (API call duration, tool execution time)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-browserstack__cap_9","uri":"capability://tool.use.integration.http.client.abstraction.with.axios.for.browserstack.api.communication","name":"http client abstraction with axios for browserstack api communication","description":"Provides a centralized HTTP client layer using Axios that handles all communication with BrowserStack's REST APIs (Test Management, Live Testing, Accessibility, App Automate, Automation, etc.). The client abstracts authentication (Basic Auth with API credentials), request/response formatting, timeout handling, and retry logic. All tool implementations use this shared client, ensuring consistent error handling and request formatting across the entire MCP server.","intents":["I want tool calls to automatically handle BrowserStack API authentication and retries","I need consistent timeout and error handling across all BrowserStack API calls","I want to monitor and debug API communication without modifying individual tool code"],"best_for":["MCP server developers extending tool functionality","Teams debugging API integration issues","Operators monitoring API communication patterns"],"limitations":["Retry logic is fixed — no per-tool customization of retry behavior","Timeout values are global — cannot be tuned per API endpoint","No request caching — all API calls result in network requests","Authentication is Basic Auth only — no OAuth or token-based auth support"],"requires":["Axios library (included in npm distribution)","BrowserStack API credentials (username and access key)","Network access to BrowserStack API endpoints"],"input_types":["HTTP method (GET, POST, PUT, DELETE)","API endpoint path","Request body (JSON)","Optional: custom headers or query parameters"],"output_types":["Parsed JSON response from BrowserStack API","HTTP status code and headers","Error objects with API error messages"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":33,"verified":false,"data_access_risk":"high","permissions":["Node.js 18+","BrowserStack API credentials (BROWSERSTACK_USERNAME and BROWSERSTACK_ACCESS_KEY environment variables)","MCP-compatible AI client (VSCode 1.80+, Cursor, Claude Desktop, or equivalent)","@modelcontextprotocol/sdk package (included in npm distribution)","BrowserStack Live Testing subscription (included in most plans)","Valid BrowserStack API credentials","Network access to BrowserStack's session infrastructure","Browser or mobile app to test","BROWSERSTACK_USERNAME environment variable set","BROWSERSTACK_ACCESS_KEY environment variable set"],"failure_modes":["Requires MCP-compatible AI client — not all IDEs support MCP protocol yet","Tool execution latency depends on BrowserStack API response times (typically 500ms-5s per call)","No built-in request queuing or rate limiting — relies on BrowserStack account limits","Stateless server design means no persistence of test context between tool calls without external storage","Live sessions consume BrowserStack concurrency limits — only N simultaneous sessions allowed per account tier","Session URLs are temporary and expire after inactivity (typically 30 minutes)","No programmatic control of browser/app actions — sessions are for human interaction only","Device availability depends on BrowserStack's real device pool — some devices may have queue times","Credentials are loaded at startup — rotation requires server restart","Environment variables are visible in process listings — requires OS-level security","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.5,"ecosystem":0.49999999999999994,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:02.371Z","last_scraped_at":"2026-05-03T14:00:15.503Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=browserstack","compare_url":"https://unfragile.ai/compare?artifact=browserstack"}},"signature":"saS3sOFFaGaQW2SYoXWAvzp0AEhAzec4q4Fw4Gs5qQ9yfxnugDHBpyS5751rixdncyaJ56M+4CtM3Mw4xau8Dg==","signedAt":"2026-06-20T11:39:51.501Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/browserstack","artifact":"https://unfragile.ai/browserstack","verify":"https://unfragile.ai/api/v1/verify?slug=browserstack","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}