{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"browserbase-mcp-server","slug":"browserbase-mcp-server","name":"Browserbase MCP Server","type":"mcp","url":"https://github.com/browserbase/mcp-server-browserbase","page_url":"https://unfragile.ai/browserbase-mcp-server","categories":["mcp-servers"],"tags":["browserbase","browser","cloud","official"],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"browserbase-mcp-server__cap_0","uri":"capability://tool.use.integration.cloud.hosted.browser.session.creation.and.lifecycle.management","name":"cloud-hosted browser session creation and lifecycle management","description":"Creates and manages isolated browser sessions in Browserbase's cloud infrastructure, handling session initialization, configuration injection (cookies, viewport dimensions, context persistence), and graceful teardown. Sessions are managed through a stagehandStore that tracks active instances and enables multi-session parallel execution without local resource constraints.","intents":["I need to spin up a fresh browser instance for each web automation task without managing local Chromium installations","I want to run multiple concurrent browser sessions in parallel without hitting my machine's memory limits","I need to inject authentication cookies or maintain persistent browser context across multiple interactions"],"best_for":["Teams building LLM agents that need scalable web automation without infrastructure overhead","Developers prototyping multi-session workflows (e.g., testing multiple user accounts simultaneously)","Enterprise applications requiring stealth mode and proxy support for anti-detection"],"limitations":["Session state is ephemeral unless contextId is explicitly provided for persistence","Network latency to cloud browsers adds 100-500ms overhead per interaction vs local browsers","Concurrent session limits depend on Browserbase account tier; no built-in queuing for overages","Viewport and browser configuration must be set at session creation time; dynamic resizing not supported"],"requires":["BROWSERBASE_API_KEY environment variable with valid Browserbase credentials","BROWSERBASE_PROJECT_ID environment variable identifying the target project","Node.js 18+ runtime for MCP server execution","Network connectivity to Browserbase cloud infrastructure"],"input_types":["CLI flags (--contextId, --browserWidth, --browserHeight, --cookies)","Environment variables (BROWSERBASE_API_KEY, BROWSERBASE_PROJECT_ID)","JSON-formatted cookie objects for session initialization"],"output_types":["Session handle/ID for subsequent interactions","Browser instance metadata (viewport dimensions, context ID)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"browserbase-mcp-server__cap_1","uri":"capability://planning.reasoning.llm.driven.web.element.interaction.with.natural.language.commands","name":"llm-driven web element interaction with natural language commands","description":"Translates natural language instructions into precise web interactions (click, fill, submit) by leveraging Stagehand's LLM-powered DOM analysis and action execution. The system parses user intent, analyzes the current page DOM, generates atomic actions, and executes them against the cloud browser, with built-in retry logic for transient failures and visual feedback through annotated screenshots.","intents":["I want to tell the LLM 'click the login button' and have it figure out the right selector without writing XPath","I need to fill a complex form with multiple fields where selectors change dynamically or are obfuscated","I want the system to retry failed interactions (e.g., element not yet visible) automatically before giving up"],"best_for":["Non-technical users building web automation workflows through natural language prompts","Developers building LLM agents that need to interact with dynamic or poorly-structured websites","QA teams automating testing workflows without maintaining brittle CSS/XPath selectors"],"limitations":["LLM-based element selection adds 500ms-2s latency per interaction due to vision processing and inference","Accuracy depends on page clarity and LLM model capability; works best on well-structured, modern websites","Shadow DOM and iframe content may not be fully visible to LLM analysis without explicit navigation","No support for complex multi-step interactions that require state tracking across page loads (e.g., multi-page forms with validation)"],"requires":["Active LLM provider API key (OpenAI, Anthropic Claude, Google Gemini, or compatible)","BROWSERBASE_API_KEY for cloud browser access","Page must be navigable and interactive (not fully client-side rendered without initial content)"],"input_types":["Natural language instruction strings (e.g., 'click the submit button')","Current page screenshot (captured automatically)","DOM context from Stagehand analysis"],"output_types":["Execution status (success/failure)","Annotated screenshot showing selected element","Error details if interaction failed"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"browserbase-mcp-server__cap_10","uri":"capability://tool.use.integration.tool.and.resource.discovery.through.mcp.protocol.introspection","name":"tool and resource discovery through mcp protocol introspection","description":"Exposes available browser automation tools and resources through MCP protocol introspection endpoints, enabling MCP clients (Claude Desktop, LLM frameworks) to discover capabilities, parameter schemas, and usage documentation without hardcoding tool definitions. The server implements MCP's tools_list and resources_list endpoints, providing JSON schemas for all browser automation operations.","intents":["I want Claude Desktop to automatically discover all available browser automation tools without manual configuration","I need to understand what parameters each tool accepts and what it returns","I'm building a custom LLM agent framework and need to dynamically load tool definitions from the MCP server"],"best_for":["Developers integrating with Claude Desktop or other MCP-aware LLM clients","Teams building dynamic LLM agent systems that discover tools at runtime","Organizations deploying multiple MCP servers and need centralized tool discovery"],"limitations":["Tool discovery is read-only; no dynamic tool registration or modification at runtime","Schema documentation is static; changes require server restart","No built-in tool versioning or deprecation warnings; clients may use outdated tool signatures","Tool schemas are JSON Schema format; complex types or conditional parameters may be difficult to express","No usage analytics or tool popularity metrics; clients can't determine which tools are most useful"],"requires":["MCP-compatible client with support for tools_list and resources_list endpoints","Network connectivity to the MCP server (STDIO or HTTP)"],"input_types":["MCP protocol introspection requests (tools_list, resources_list)"],"output_types":["JSON array of tool definitions with schemas","JSON array of resource definitions with descriptions","Tool parameter schemas (JSON Schema format)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"browserbase-mcp-server__cap_11","uri":"capability://automation.workflow.error.handling.and.interaction.retry.logic.with.exponential.backoff","name":"error handling and interaction retry logic with exponential backoff","description":"Implements automatic retry logic for transient failures (element not visible, network timeouts, JavaScript errors) with exponential backoff and configurable retry limits, built into Stagehand's action execution layer. Failed interactions are automatically retried with increasing delays (100ms, 200ms, 400ms, etc.) up to a maximum number of attempts, with detailed error reporting for permanent failures.","intents":["I want the system to automatically retry clicking a button if it's not visible yet (e.g., loading animation)","I need robust error handling for flaky websites with intermittent timeouts or JavaScript errors","I want detailed error messages when interactions fail permanently so I can debug the issue"],"best_for":["Automation workflows on flaky or slow-loading websites with intermittent failures","Production systems that need resilience to transient network or browser errors","Developers debugging automation failures and needing detailed error context"],"limitations":["Retry logic is automatic and not configurable per-tool; no per-interaction retry tuning","Exponential backoff may add significant latency (up to 10+ seconds) for heavily-retried interactions","No distinction between retryable and permanent errors; some errors (invalid selector, permission denied) are retried unnecessarily","Retry logic is opaque to the LLM; the agent doesn't know if an interaction succeeded on first try or after retries","No built-in circuit breaker; if a site is completely down, all interactions will timeout after max retries"],"requires":["Active cloud browser session with Browserbase","Stagehand library integration (included in mcp-server-browserbase)"],"input_types":["Interaction request (click, fill, navigate, etc.)","Implicit retry configuration (built-in defaults)"],"output_types":["Interaction success/failure status","Detailed error message with retry count and final error","Annotated screenshot showing failure context"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"browserbase-mcp-server__cap_2","uri":"capability://image.visual.screenshot.capture.with.optional.llm.powered.visual.annotation","name":"screenshot capture with optional llm-powered visual annotation","description":"Captures full-page or viewport screenshots from the cloud browser and optionally annotates them with LLM-generated labels identifying interactive elements, form fields, and content regions. Annotations are overlaid on the screenshot to help LLMs understand page structure without requiring DOM parsing, enabling vision-based page analysis and debugging of automation workflows.","intents":["I need to see what the page looks like at a specific point in the automation to debug failures","I want the LLM to analyze a screenshot and identify clickable elements without parsing HTML","I need annotated screenshots for documentation or testing reports showing what elements were identified"],"best_for":["Developers debugging LLM-driven automation workflows visually","Teams building visual testing or screenshot-based regression testing","Agents that need to understand page layout without DOM access (e.g., heavily obfuscated sites)"],"limitations":["Full-page screenshots may be very large (10-50MB) for long pages; no built-in compression or tiling","Annotation accuracy depends on LLM vision capabilities; may miss small or dynamically-rendered elements","Screenshots capture rendered state only; hidden elements (display:none, visibility:hidden) are not visible","Annotation adds 1-3s latency per screenshot due to LLM inference"],"requires":["Active cloud browser session with Browserbase","LLM provider API key if annotations are requested (OpenAI, Claude, Gemini, etc.)"],"input_types":["Session handle/ID","Optional annotation request flag"],"output_types":["PNG or JPEG image buffer","Optional JSON array of annotated elements with bounding boxes and labels"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"browserbase-mcp-server__cap_3","uri":"capability://data.processing.analysis.structured.data.extraction.from.web.pages.with.llm.powered.content.analysis","name":"structured data extraction from web pages with llm-powered content analysis","description":"Extracts structured data (JSON, CSV, tables) from web pages by leveraging LLM-powered content analysis to identify and parse relevant information without requiring predefined schemas or CSS selectors. The system analyzes page content, infers data structure, and returns normalized output, with support for multi-page extraction and pagination handling through Stagehand's automation capabilities.","intents":["I need to scrape product listings from an e-commerce site and extract price, title, and rating into JSON without writing selectors","I want to extract data from a paginated table and automatically navigate through all pages","I need to handle dynamic content that loads via JavaScript without writing complex wait logic"],"best_for":["Data engineers building web scraping pipelines without maintaining brittle selectors","Researchers collecting data from multiple sources with varying HTML structures","LLM agents that need to extract insights from web pages as part of larger workflows"],"limitations":["LLM-based extraction is slower (2-5s per page) than selector-based scraping due to inference overhead","Accuracy depends on page clarity and LLM model; may hallucinate or miss subtle data variations","No built-in deduplication or data validation; requires post-processing for data quality","Large pages (>100KB HTML) may exceed LLM context windows; requires chunking or summarization","Pagination handling requires explicit navigation instructions; no automatic detection of 'next page' patterns"],"requires":["Active cloud browser session","LLM provider API key (OpenAI, Claude, Gemini, etc.)","Target page must be navigable and contain extractable content"],"input_types":["Natural language extraction instruction (e.g., 'extract all product names and prices')","Optional schema hint (JSON structure or field names)","Current page content (HTML or screenshot)"],"output_types":["JSON object or array with extracted data","CSV format for tabular data","Extraction metadata (confidence scores, missing fields)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"browserbase-mcp-server__cap_4","uri":"capability://tool.use.integration.multi.provider.llm.model.selection.and.fallback.routing","name":"multi-provider llm model selection and fallback routing","description":"Supports dynamic selection of LLM providers (OpenAI, Anthropic Claude, Google Gemini, and compatible APIs) for driving web automation and content analysis, with configurable model names and automatic fallback routing if a provider is unavailable. Configuration is managed through CLI flags (--modelName) and environment variables, enabling runtime model switching without code changes.","intents":["I want to use Claude for web automation but fall back to GPT-4 if Claude API is down","I need to use a specific model version (e.g., gpt-4-turbo) for better accuracy on complex pages","I want to route different tasks to different models based on cost or capability (e.g., GPT-3.5 for simple clicks, GPT-4 for data extraction)"],"best_for":["Teams with multi-model strategies or cost optimization requirements","Developers building resilient agents that need provider redundancy","Organizations evaluating different LLM providers for web automation tasks"],"limitations":["No built-in load balancing or cost tracking across providers; requires external monitoring","Fallback routing is manual (via configuration) rather than automatic; no intelligent retry with different models","Model-specific capabilities (vision, function calling) must be manually verified; no capability detection","Context and conversation history are not preserved across model switches; each request is independent"],"requires":["API keys for at least one LLM provider (OPENAI_API_KEY, ANTHROPIC_API_KEY, GEMINI_API_KEY, etc.)","Model name must be valid for the selected provider (e.g., 'gpt-4-turbo' for OpenAI, 'claude-3-opus' for Anthropic)"],"input_types":["CLI flag --modelName <model_identifier>","Environment variables for API keys (provider-specific)"],"output_types":["Model selection confirmation","Fallback status if primary provider unavailable"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"browserbase-mcp-server__cap_5","uri":"capability://safety.moderation.enterprise.anti.detection.and.stealth.mode.configuration","name":"enterprise anti-detection and stealth mode configuration","description":"Provides advanced anti-detection capabilities through Browserbase's stealth mode and proxy support, configurable via CLI flags (--advancedStealth, --proxies) to mask automation signatures and evade bot detection. Stealth mode modifies browser fingerprints, disables detection APIs (navigator.webdriver), and rotates user agents, while proxy support enables geographic spoofing and IP rotation for compliance with regional restrictions.","intents":["I need to scrape a site that blocks automated traffic; stealth mode should help avoid detection","I want to test my website's bot detection by simulating an automated visitor","I need to access content from different geographic regions using proxies without being blocked"],"best_for":["Enterprise teams automating workflows on sites with aggressive bot detection","Security researchers testing anti-bot measures and bot detection systems","Compliance-focused applications that need geographic IP rotation for regional access"],"limitations":["Stealth mode is not foolproof; sophisticated detection (behavioral analysis, ML-based) may still identify automation","Proxy support adds latency (100-500ms per request) and depends on proxy provider reliability","Stealth mode may break legitimate functionality (e.g., some sites disable features for non-standard browsers)","No built-in detection of when stealth mode is insufficient; requires manual testing and iteration","Proxy rotation is manual; no automatic failover or load balancing across proxy pools"],"requires":["BROWSERBASE_API_KEY with stealth mode and proxy features enabled (may require premium tier)","CLI flags --advancedStealth and/or --proxies to enable features","Proxy server credentials if using proxy support"],"input_types":["CLI flag --advancedStealth (boolean)","CLI flag --proxies (boolean or proxy configuration)"],"output_types":["Browser session with stealth mode enabled","Proxy configuration confirmation"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"browserbase-mcp-server__cap_6","uri":"capability://tool.use.integration.mcp.protocol.transport.abstraction.with.stdio.and.http.support","name":"mcp protocol transport abstraction with stdio and http support","description":"Implements the Model Context Protocol (MCP) specification with support for multiple transport mechanisms (STDIO for local/subprocess communication, HTTP/HTTPS for remote clients), enabling flexible deployment across different LLM application architectures. The server exposes tools and resources through standardized MCP endpoints, allowing any MCP-compatible client (Claude Desktop, LLM frameworks, custom agents) to invoke browser automation capabilities.","intents":["I want to use this MCP server with Claude Desktop without writing custom integration code","I need to deploy the browser automation server remotely and access it from multiple LLM applications","I'm building a custom LLM agent framework and need a standard protocol for tool integration"],"best_for":["Developers integrating with Claude Desktop or other MCP-compatible LLM clients","Teams building multi-tool LLM agent systems that need standardized tool interfaces","Organizations deploying browser automation as a shared service across multiple applications"],"limitations":["STDIO transport is limited to local/subprocess communication; not suitable for distributed systems","HTTP transport requires manual authentication/authorization; no built-in OAuth or API key management","MCP protocol overhead adds ~50-100ms per tool invocation compared to direct library calls","No built-in rate limiting or request queuing; high-volume clients may overwhelm the server","Transport selection is fixed at server startup; no dynamic switching between STDIO and HTTP"],"requires":["Node.js 18+ runtime","MCP-compatible client (Claude Desktop, LLM framework with MCP support, or custom implementation)","For HTTP transport: network connectivity and optional TLS certificates"],"input_types":["MCP protocol messages (JSON-RPC format)","Tool call requests with parameters","Resource access requests"],"output_types":["MCP protocol responses (JSON-RPC format)","Tool execution results","Resource content (text, binary)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"browserbase-mcp-server__cap_7","uri":"capability://memory.knowledge.persistent.browser.context.and.session.state.management","name":"persistent browser context and session state management","description":"Maintains browser state across multiple interactions through persistent context IDs (--contextId CLI flag), enabling multi-step workflows where authentication, cookies, and DOM state are preserved between tool invocations. Context is stored in Browserbase's cloud infrastructure, allowing LLM agents to maintain session continuity without re-authenticating or re-navigating to previous pages.","intents":["I need to log in once and then perform multiple actions on an authenticated page without re-authenticating","I want to maintain browser history and cookies across multiple LLM tool calls in a single workflow","I need to preserve form state or scroll position between interactions for complex multi-step processes"],"best_for":["LLM agents performing multi-step workflows (e.g., login → search → purchase)","Applications requiring session continuity across multiple tool invocations","Teams building stateful automation workflows that depend on preserved authentication"],"limitations":["Context persistence is tied to Browserbase account; contexts may expire after inactivity (duration depends on tier)","No built-in context cleanup; expired or unused contexts consume storage and may incur costs","Context IDs are opaque strings; no visibility into context contents or metadata","Concurrent modifications to the same context from multiple clients may cause race conditions","Context switching (using different contextId) requires explicit tool invocation; no automatic context management"],"requires":["BROWSERBASE_API_KEY with context persistence support","CLI flag --contextId <context_id> to enable persistence (optional; generates new context if not provided)","Browserbase account tier that supports persistent contexts"],"input_types":["Context ID string (provided by Browserbase or generated on first use)","Optional context configuration (cookies, viewport, etc.)"],"output_types":["Context ID for subsequent invocations","Context metadata (creation time, last accessed, state summary)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"browserbase-mcp-server__cap_8","uri":"capability://automation.workflow.viewport.and.browser.configuration.injection.at.session.creation","name":"viewport and browser configuration injection at session creation","description":"Configures browser viewport dimensions, user agent, and other browser properties at session creation time through CLI flags (--browserWidth, --browserHeight) and environment variables, enabling consistent rendering across different screen sizes and device types. Configuration is applied at the Browserbase cloud browser level, ensuring all subsequent interactions use the specified viewport without requiring client-side resizing.","intents":["I need to test how a website renders on mobile (375x667) vs desktop (1920x1080) viewports","I want to ensure consistent screenshots across multiple automation runs by fixing viewport dimensions","I need to simulate a specific device type (e.g., iPhone, tablet) for testing responsive designs"],"best_for":["QA teams testing responsive web design across multiple viewports","Developers validating website rendering on different screen sizes","Automation workflows that require consistent visual output for screenshot comparison"],"limitations":["Viewport configuration is immutable after session creation; dynamic resizing requires creating a new session","User agent spoofing is limited to predefined options; custom user agents may not be supported","Viewport dimensions are applied at the browser level; some websites may override or ignore viewport hints","No support for advanced device emulation (touch events, device sensors, network throttling) beyond viewport","Configuration changes require session recreation, adding latency and resource overhead"],"requires":["BROWSERBASE_API_KEY for cloud browser access","CLI flags --browserWidth and --browserHeight with numeric pixel values","Valid viewport dimensions (typically 320-3840 pixels width, 240-2160 pixels height)"],"input_types":["CLI flags --browserWidth <pixels> and --browserHeight <pixels>","Optional user agent configuration"],"output_types":["Session confirmation with applied viewport dimensions","Browser metadata (actual viewport, device type)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"browserbase-mcp-server__cap_9","uri":"capability://automation.workflow.cookie.and.authentication.credential.injection.for.session.initialization","name":"cookie and authentication credential injection for session initialization","description":"Injects authentication cookies and credentials into browser sessions at creation time through CLI flags (--cookies with JSON format) and environment variables, enabling pre-authenticated sessions without requiring login automation. Cookies are applied to the cloud browser before any navigation, ensuring all subsequent requests include authentication headers and session tokens.","intents":["I want to start with an already-logged-in session so I don't need to automate the login flow","I need to inject session tokens or API keys as cookies for authenticated API testing","I want to test authenticated features without storing credentials in code or environment variables"],"best_for":["Automation workflows that need to skip login steps and go directly to authenticated features","Testing authenticated APIs or protected pages without hardcoding credentials","Multi-user testing scenarios where different sessions need different authentication states"],"limitations":["Cookies must be provided in JSON format; no automatic cookie extraction or serialization from existing sessions","Cookie injection happens before navigation; cookies set by JavaScript during page load are not preserved","No validation of cookie format or expiration; invalid cookies may cause silent failures","Cookies are session-specific; they don't persist across different context IDs or browser instances","No support for complex authentication schemes (OAuth, SAML, multi-factor); only cookie-based auth is supported"],"requires":["BROWSERBASE_API_KEY for cloud browser access","CLI flag --cookies with valid JSON array of cookie objects (name, value, domain, path, etc.)","Cookies must be in valid format: [{\"name\": \"...\", \"value\": \"...\", \"domain\": \"...\"}]"],"input_types":["JSON array of cookie objects with properties: name, value, domain, path, secure, httpOnly, sameSite","Optional cookie expiration timestamps"],"output_types":["Session confirmation with injected cookies","Cookie validation status (success/failure)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"browserbase-mcp-server__headline","uri":"capability://automation.workflow.mcp.server.for.cloud.browser.automation","name":"mcp server for cloud browser automation","description":"The Browserbase MCP Server is a powerful solution that enables Large Language Models to automate web browser interactions through cloud-hosted browsers, providing a seamless integration for web automation tasks.","intents":["best MCP server for web automation","cloud browser automation for LLMs","MCP server for browser interactions","how to automate web tasks with LLMs","top tools for cloud browser automation"],"best_for":["developers needing web automation","teams using LLMs for web tasks"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":75,"verified":false,"data_access_risk":"high","permissions":["BROWSERBASE_API_KEY environment variable with valid Browserbase credentials","BROWSERBASE_PROJECT_ID environment variable identifying the target project","Node.js 18+ runtime for MCP server execution","Network connectivity to Browserbase cloud infrastructure","Active LLM provider API key (OpenAI, Anthropic Claude, Google Gemini, or compatible)","BROWSERBASE_API_KEY for cloud browser access","Page must be navigable and interactive (not fully client-side rendered without initial content)","MCP-compatible client with support for tools_list and resources_list endpoints","Network connectivity to the MCP server (STDIO or HTTP)","Active cloud browser session with Browserbase"],"failure_modes":["Session state is ephemeral unless contextId is explicitly provided for persistence","Network latency to cloud browsers adds 100-500ms overhead per interaction vs local browsers","Concurrent session limits depend on Browserbase account tier; no built-in queuing for overages","Viewport and browser configuration must be set at session creation time; dynamic resizing not supported","LLM-based element selection adds 500ms-2s latency per interaction due to vision processing and inference","Accuracy depends on page clarity and LLM model capability; works best on well-structured, modern websites","Shadow DOM and iframe content may not be fully visible to LLM analysis without explicit navigation","No support for complex multi-step interactions that require state tracking across page loads (e.g., multi-page forms with validation)","Tool discovery is read-only; no dynamic tool registration or modification at runtime","Schema documentation is static; changes require server restart","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.52,"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":"2026-06-17T09:51:04.690Z","last_scraped_at":null,"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=browserbase-mcp-server","compare_url":"https://unfragile.ai/compare?artifact=browserbase-mcp-server"}},"signature":"pnzDPXEyxJpcrd1i1gk8dr+/DOegKAsjg6DGQ1U1I+rudojWT1+j1nyuwAitPfkgHOAfLfWZYRM0G45RoolqCw==","signedAt":"2026-06-22T10:27:12.147Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/browserbase-mcp-server","artifact":"https://unfragile.ai/browserbase-mcp-server","verify":"https://unfragile.ai/api/v1/verify?slug=browserbase-mcp-server","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"}}