{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"vercel","slug":"vercel","name":"Vercel","type":"platform","url":"https://vercel.com","page_url":"https://unfragile.ai/vercel","categories":["deployment-infra"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":"$20/mo"},"status":"active","verified":false},"capabilities":[{"id":"vercel__cap_0","uri":"capability://automation.workflow.git.triggered.automatic.deployment.with.preview.environments","name":"git-triggered automatic deployment with preview environments","description":"Automatically deploys web applications on every Git push to connected repositories (GitHub, GitLab, Bitbucket) with zero configuration required. Creates isolated preview environments for pull requests and branches, enabling teams to test changes before merging to production. Uses webhook-based triggers from Git providers to initiate build and deployment pipelines without manual intervention or CI/CD configuration.","intents":["Deploy my Next.js app automatically whenever I push to main branch","Share a live preview URL with teammates for PR review before merging","Skip writing CI/CD configuration and get deployment working immediately","Test feature branches in production-like environments without affecting live site"],"best_for":["Solo developers and small teams building web applications","Teams migrating from manual deployment or complex CI/CD setups","Projects using Next.js, Nuxt, Svelte, or other supported frameworks"],"limitations":["Automatic deployment on every push may not suit teams requiring manual approval gates","Build time limits and parallelization details are undocumented","Preview environments are ephemeral and tied to branch lifetime","No documented support for monorepo selective deployment (deploy only changed packages)"],"requires":["Git repository on GitHub, GitLab, or Bitbucket","Vercel account (free tier available)","Web application using supported framework (Next.js, Nuxt, Svelte, etc.)"],"input_types":["Git repository URL","Source code (JavaScript/TypeScript/Python)","Environment variables (optional)"],"output_types":["Live deployment URL","Preview environment URL per branch/PR","Build logs and deployment status"],"categories":["automation-workflow","ci/cd"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vercel__cap_1","uri":"capability://automation.workflow.edge.function.execution.with.global.points.of.presence","name":"edge function execution with global points of presence","description":"Executes serverless functions at Vercel's edge network (global Points of Presence) with automatic routing and latency optimization. Functions run closer to users geographically, reducing response time compared to centralized cloud regions. Supports streaming responses and integrates with Vercel's AI SDK for real-time AI workloads. Pricing is per-request with included quotas (1M/month Hobby, 10M/month Pro) and overage charges of $2 per 1M requests.","intents":["Run API endpoints with minimal latency by executing code near users globally","Stream AI responses (chat, completions) directly to clients without buffering","Implement real-time features like live search, autocomplete, or notifications","Reduce cold start latency for frequently-accessed functions"],"best_for":["Applications requiring sub-100ms response times globally","AI-powered features (chatbots, agents, streaming completions)","High-traffic APIs with variable load patterns","Teams building real-time applications without managing infrastructure"],"limitations":["Cold start latency is not quantified; 'cold start prevention' is Pro-only feature with unknown performance impact","Function timeout limits and maximum concurrent execution limits are undocumented","No GPU support documented; Fluid Compute appears CPU-only","Edge request quotas may be insufficient for high-volume APIs (10M/month Pro = ~3.8K requests/hour average)","Regional pricing details are unknown; potential cost variance across regions"],"requires":["Vercel account (Pro tier recommended for production workloads)","API key for external services (OpenAI, Anthropic, etc.) if using AI features","TypeScript or JavaScript runtime (Node.js 18+)"],"input_types":["HTTP requests (GET, POST, PUT, DELETE)","JSON payloads","Query parameters","Request headers"],"output_types":["HTTP responses (JSON, text, streams)","Streaming responses (Server-Sent Events, chunked transfer encoding)","Status codes and headers"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vercel__cap_10","uri":"capability://automation.workflow.custom.domain.management.with.automatic.tls.ssl.certificates","name":"custom domain management with automatic tls/ssl certificates","description":"Manages custom domains for deployed applications with automatic TLS/SSL certificate provisioning and renewal. Supports multiple domains per application and automatic HTTPS enforcement. Certificates are provisioned automatically without manual configuration or renewal management. Integrates with DNS providers for automatic domain verification. All traffic is encrypted end-to-end.","intents":["Use custom domain for deployed application instead of vercel.app subdomain","Enable HTTPS for custom domains without certificate management","Support multiple domains and subdomains for single application","Enforce HTTPS and redirect HTTP traffic automatically"],"best_for":["Production applications requiring branded domains","SaaS platforms with customer-specific subdomains","Teams avoiding certificate management overhead","Applications requiring HTTPS compliance"],"limitations":["DNS provider integration details and supported providers are undocumented","Certificate renewal process and timeline are undocumented","Wildcard domain support is undocumented","DNS propagation time and verification process are undocumented","No documented support for custom certificate uploads","HTTPS enforcement options and redirect behavior are undocumented"],"requires":["Vercel account","Custom domain ownership","DNS access to configure domain records"],"input_types":["Domain name","DNS records (CNAME, A, TXT)"],"output_types":["HTTPS-enabled domain","Certificate details","DNS configuration status"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vercel__cap_11","uri":"capability://automation.workflow.feature.flags.and.deployment.controls.with.toolbar.integration","name":"feature flags and deployment controls with toolbar integration","description":"Provides feature flag management integrated into Vercel's in-browser toolbar. Enables toggling features on/off in production without redeployment. Toolbar provides live feature flag controls for testing and gradual rollouts. Integrates with deployment pipeline for A/B testing and canary deployments. Supports targeting flags to specific users, regions, or traffic percentages.","intents":["Toggle features on/off in production without redeploying","Test features with specific users or traffic percentages","Implement gradual rollouts and canary deployments","A/B test features and measure impact on user behavior"],"best_for":["Teams implementing continuous deployment with feature flags","Product teams testing features with subsets of users","Organizations requiring gradual rollout capabilities","Teams measuring feature impact through A/B testing"],"limitations":["Feature flag targeting options and syntax are undocumented","Flag evaluation performance and latency are undocumented","Persistence and storage of flag state are undocumented","API for programmatic flag management is undocumented","Integration with analytics for A/B test measurement is undocumented","Pricing and usage limits for feature flags are undocumented"],"requires":["Vercel account","Feature flag definitions","Application code to check flag state"],"input_types":["Flag name and configuration","Targeting rules (user, region, percentage)","Flag value (boolean, string, JSON)"],"output_types":["Flag state (enabled/disabled)","Targeted flag values","Analytics data (if integrated)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vercel__cap_12","uri":"capability://data.processing.analysis.vercel.storage.with.database.and.file.storage.options","name":"vercel storage with database and file storage options","description":"Provides integrated storage solutions for deployed applications including database and file storage options. Supports multiple storage backends (details undocumented). Integrates with deployment pipeline for automatic provisioning and configuration. Enables applications to persist data without managing external databases. Pricing is usage-based with included quotas on paid tiers.","intents":["Store application data without managing external database","Persist files uploaded by users","Query data from deployed applications","Scale storage automatically with application traffic"],"best_for":["Applications requiring simple data persistence","Teams avoiding external database management","SaaS platforms with variable storage needs","Startups building MVPs without infrastructure overhead"],"limitations":["Supported storage types (SQL, NoSQL, object storage) are undocumented","Database query language and API are undocumented","Storage limits and scaling behavior are undocumented","Backup and disaster recovery options are undocumented","Data residency and compliance options are undocumented","Migration tools for existing databases are undocumented","No documented support for complex queries or transactions"],"requires":["Vercel account (Pro tier or higher for storage)","Application code to interact with storage API"],"input_types":["Data to store (structured or files)","Query parameters","File uploads"],"output_types":["Stored data","Query results","File URLs"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vercel__cap_13","uri":"capability://automation.workflow.environment.variable.management.with.deployment.specific.configuration","name":"environment variable management with deployment-specific configuration","description":"Manages environment variables for deployed applications with support for deployment-specific overrides. Variables can be set per environment (development, preview, production) and per deployment. Integrates with Git-based deployment for automatic environment configuration. Supports secrets management for sensitive values (API keys, database credentials). Variables are injected at build time and runtime.","intents":["Configure API keys and secrets without committing to Git","Use different configurations for development, staging, and production","Manage environment-specific settings (database URLs, API endpoints)","Rotate secrets without redeploying application"],"best_for":["Teams managing multiple deployment environments","Applications requiring sensitive configuration (API keys, database credentials)","Teams following security best practices for secrets management","Developers needing environment-specific configuration"],"limitations":["Secrets encryption and storage details are undocumented","Access control for environment variables is undocumented","Audit logging for variable access is undocumented","Variable rotation and versioning are undocumented","Integration with external secrets managers is undocumented","No documented support for variable interpolation or templating"],"requires":["Vercel account","Environment variable definitions","Application code to read variables"],"input_types":["Variable name and value","Environment target (development, preview, production)","Deployment-specific overrides"],"output_types":["Injected environment variables","Build-time configuration","Runtime configuration"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vercel__cap_14","uri":"capability://automation.workflow.incremental.static.regeneration.isr.for.scheduled.content.updates","name":"incremental static regeneration (isr) for scheduled content updates","description":"Enables static pages to be regenerated on a schedule without full site rebuilds. Pages are cached at edge and regenerated in the background at specified intervals. Supports on-demand regeneration triggered by webhooks or API calls. Combines static site performance with dynamic content updates. Reduces build times and server load compared to server-side rendering.","intents":["Update static content on schedule without rebuilding entire site","Regenerate pages when content changes (triggered by CMS webhooks)","Serve cached static pages for performance while updating in background","Reduce build times for large sites with frequently-updated content"],"best_for":["Content-heavy websites with frequently-updated pages","E-commerce sites with product catalog updates","News and publishing platforms with regular content updates","Teams requiring fast page loads with dynamic content"],"limitations":["Regeneration schedule granularity and timing are undocumented","On-demand regeneration API and webhook format are undocumented","Regeneration failure handling and retry logic are undocumented","Cache invalidation options are undocumented","No documented support for partial page regeneration","Regeneration latency and background job execution time are undocumented"],"requires":["Next.js application (ISR is Next.js feature)","Vercel deployment","Pages configured with revalidate parameter"],"input_types":["Revalidation interval (seconds)","On-demand regeneration requests","Webhook triggers from CMS"],"output_types":["Regenerated static pages","Cache headers","Regeneration status"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vercel__cap_15","uri":"capability://image.visual.image.optimization.with.automatic.format.conversion.and.responsive.sizing","name":"image optimization with automatic format conversion and responsive sizing","description":"Automatically optimizes images for web delivery with format conversion (WebP, AVIF), responsive sizing, and lazy loading. Serves optimized images from edge network for fast delivery. Supports dynamic image resizing based on device and viewport. Reduces image file sizes and improves page load performance. Integrates with Next.js Image component for seamless usage.","intents":["Reduce image file sizes automatically without manual optimization","Serve different image formats based on browser support (WebP, AVIF)","Deliver appropriately-sized images for different devices and viewports","Improve page load performance through optimized image delivery"],"best_for":["Image-heavy websites (e-commerce, media, portfolios)","Teams without image optimization expertise","Applications requiring fast page loads with many images","Mobile-first applications with bandwidth constraints"],"limitations":["Supported image formats and conversion options are undocumented","Responsive sizing breakpoints and algorithm are undocumented","Lazy loading behavior and intersection observer configuration are undocumented","Cache behavior and CDN edge caching are undocumented","No documented support for animated images (GIF, APNG)","Image transformation API and parameters are undocumented"],"requires":["Vercel deployment","Next.js Image component (recommended) or direct image URLs","Images hosted on Vercel or external CDN"],"input_types":["Image files (JPEG, PNG, WebP, etc.)","Image URLs","Responsive sizing parameters"],"output_types":["Optimized images (WebP, AVIF)","Responsive image sets (srcset)","Lazy-loaded images"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vercel__cap_2","uri":"capability://automation.workflow.fluid.compute.serverless.infrastructure.with.provisioned.memory","name":"fluid compute serverless infrastructure with provisioned memory","description":"Provides 'servers in serverless form' — configurable provisioned memory with active CPU allocation designed for AI workloads. Abstracts away server management while offering more control than traditional edge functions. Automatically scales based on traffic and integrates with Vercel's deployment pipeline. Pricing is usage-based with included credits on Pro tier ($20/month includes $20 usage credit).","intents":["Run memory-intensive AI workloads (LLM inference, embeddings, vector operations) without managing servers","Configure memory allocation for specific application requirements","Scale automatically from zero to handle traffic spikes without provisioning infrastructure","Deploy long-running processes or batch jobs without managing containers"],"best_for":["AI-powered applications requiring more memory than edge functions","Teams avoiding infrastructure management (Kubernetes, Docker, VMs)","Applications with variable traffic patterns requiring auto-scaling","Startups and small teams building AI agents or agentic workloads"],"limitations":["Memory tiers and CPU specifications are completely undocumented","No quantified cold start latency or warm-up behavior documented","Function timeout limits are unknown","Maximum concurrent execution limits are unknown","No GPU support documented; appears CPU-only","Pricing model is opaque — usage-based charges beyond included credits are not detailed"],"requires":["Vercel Pro account or higher ($20/month minimum)","Application code compatible with Node.js runtime","API keys for external AI services if using LLM providers"],"input_types":["HTTP requests","JSON payloads","Environment variables for configuration"],"output_types":["HTTP responses","Streaming responses","Structured data (JSON)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vercel__cap_3","uri":"capability://text.generation.language.vercel.ai.sdk.with.streaming.and.tool.calling","name":"vercel ai sdk with streaming and tool calling","description":"TypeScript toolkit that abstracts language model APIs (OpenAI, Anthropic, etc.) with native support for streaming responses and schema-based function calling. Handles streaming protocol details (Server-Sent Events, chunked encoding) automatically, enabling real-time AI responses in web applications. Integrates with Vercel's edge functions and Fluid Compute for seamless deployment. Supports tool calling via structured schema registry for function-based agent interactions.","intents":["Build chat interfaces with streaming responses without managing HTTP streaming manually","Implement AI agents with tool calling by defining function schemas and handlers","Switch between LLM providers (OpenAI, Anthropic, Ollama) with minimal code changes","Deploy AI features to edge functions without vendor-specific API boilerplate"],"best_for":["Frontend developers building AI-powered web applications","Teams building chatbots and conversational interfaces","Developers implementing AI agents with tool calling","Projects requiring multi-provider LLM support with unified API"],"limitations":["TypeScript/JavaScript only — no Python SDK documented","Specific LLM providers supported are undocumented (assumed OpenAI, Anthropic, Ollama but not verified)","Tool calling schema format and validation rules are undocumented","No built-in persistence — requires external state store for conversation history","Streaming abstractions add ~200ms latency per chain step (estimated, not documented)","No built-in rate limiting or retry logic documented"],"requires":["Node.js 18+ runtime","TypeScript or JavaScript","API key for at least one supported LLM provider","Vercel account for deployment (free tier sufficient for development)"],"input_types":["User messages (text)","Tool definitions (JSON schema)","System prompts (text)","Conversation history (array of messages)"],"output_types":["Streamed text responses (Server-Sent Events)","Tool call requests (structured JSON)","Structured completions (JSON)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vercel__cap_4","uri":"capability://safety.moderation.web.application.firewall.with.ddos.mitigation.and.bot.management","name":"web application firewall with ddos mitigation and bot management","description":"Provides automatic DDoS mitigation and configurable WAF rules across all tiers. Hobby tier includes basic DDoS protection; Pro tier adds up to 40 custom WAF rules and 100 IP blocks; Enterprise tier scales to 1,000 custom rules with OWASP Core Ruleset management. Bot management includes AI-powered bot detection (managed ruleset) and BotID invisible CAPTCHA ($1 per 1,000 deep analysis checks). All traffic is encrypted with automatic TLS/SSL certificates.","intents":["Protect web applications from DDoS attacks without manual configuration","Block malicious traffic using custom WAF rules based on request patterns","Prevent bot abuse (scrapers, credential stuffing) with AI-powered detection","Implement invisible CAPTCHA for high-risk requests without user friction"],"best_for":["Public-facing web applications receiving untrusted traffic","SaaS platforms and APIs vulnerable to bot abuse","Teams requiring compliance with security standards (WAF rules)","High-traffic applications needing DDoS protection at scale"],"limitations":["DDoS mitigation is automatic but details on attack types handled are undocumented","Custom WAF rule syntax and capabilities are undocumented","BotID pricing ($1 per 1,000 checks) may be expensive for high-traffic sites (1M requests = $1,000/month)","OWASP Core Ruleset is Enterprise-only, limiting Pro tier compliance options","No documented SLA for DDoS mitigation response time","Bot detection accuracy and false positive rates are undocumented"],"requires":["Vercel account (Hobby tier for basic DDoS, Pro for custom rules, Enterprise for OWASP)","Custom WAF rules require Pro tier ($20/month minimum)","BotID requires separate per-check billing"],"input_types":["HTTP requests (headers, body, IP address)","WAF rule definitions (custom patterns)","IP blocklists"],"output_types":["Allow/block decision per request","CAPTCHA challenge (BotID)","Security logs and analytics"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vercel__cap_5","uri":"capability://data.processing.analysis.performance.monitoring.with.speed.insights.and.analytics","name":"performance monitoring with speed insights and analytics","description":"Provides real-time performance metrics (Speed Insights) and traffic analytics integrated into the Vercel dashboard. Tracks Core Web Vitals, page load times, and user experience metrics. Includes in-browser toolbar for live performance inspection, layout analysis, and accessibility checking. Analytics dashboard shows traffic patterns, deployment performance, and regional performance distribution. Integrated with observability product for tracing every step of request execution.","intents":["Monitor Core Web Vitals and page load performance in production","Identify performance regressions after deployments","Analyze traffic patterns and geographic distribution of users","Debug layout issues and accessibility problems in live deployments","Compare performance across deployments and track improvements over time"],"best_for":["Frontend teams optimizing user experience and Core Web Vitals","Product managers tracking application performance impact on business metrics","Teams debugging performance issues in production","Developers building performance-critical applications (e-commerce, content platforms)"],"limitations":["Observability product details (tracing, distributed tracing support) are undocumented","Speed Insights metrics and sampling methodology are undocumented","Analytics retention period and data granularity are undocumented","No documented integration with external APM tools (DataDog, New Relic, etc.)","Toolbar features (feedback, feature flags, content editing) are undocumented in detail","No API documented for programmatic access to metrics"],"requires":["Vercel account (Analytics included in Hobby+ tiers)","Deployed application on Vercel","JavaScript enabled in browser for toolbar features"],"input_types":["Application traffic (automatic collection)","User interactions (automatic via toolbar)","Deployment metadata (automatic)"],"output_types":["Performance metrics (Core Web Vitals, load times)","Traffic analytics (requests, geographic distribution)","Performance trends and comparisons","Trace logs and request details"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vercel__cap_6","uri":"capability://code.generation.editing.v0.ai.powered.ui.generation.and.application.scaffolding","name":"v0 ai-powered ui generation and application scaffolding","description":"AI assistant that generates React/Next.js UI components and full applications from natural language descriptions. Uses generative AI to produce production-ready code with Tailwind CSS styling. Integrates with Vercel's deployment pipeline for one-click deployment of generated applications. Supports iterative refinement through conversation-based prompts. Generated code is editable and deployable without modification.","intents":["Generate UI components from text descriptions without writing CSS or HTML","Scaffold new applications quickly for prototyping and MVPs","Create landing pages, dashboards, and admin interfaces with AI assistance","Iterate on designs through conversational prompts without design tools"],"best_for":["Non-technical founders and product managers prototyping MVPs","Frontend developers accelerating UI development","Teams building internal tools and admin dashboards","Designers without coding experience building interactive prototypes"],"limitations":["Generated code quality and production-readiness are undocumented","Customization limitations for complex or unique designs are undocumented","No documented support for component libraries or design systems","Iterative refinement capabilities and conversation context limits are undocumented","No documented support for non-React frameworks","Pricing and usage limits for v0 are undocumented"],"requires":["Vercel account","v0 access (availability and pricing unknown)","Natural language description of desired UI"],"input_types":["Natural language prompts describing UI","Iterative refinement prompts","Design references or screenshots (assumed)"],"output_types":["React/Next.js component code","Tailwind CSS styling","Full application scaffolds","Deployable applications"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vercel__cap_7","uri":"capability://planning.reasoning.vercel.agents.for.autonomous.workflows.and.conversational.interfaces","name":"vercel agents for autonomous workflows and conversational interfaces","description":"Framework for building autonomous AI agents and conversational interfaces that can interact with external systems. Agents use tool calling to invoke functions, APIs, and MCP servers. Supports multi-turn conversations with memory and context management. Integrates with Vercel's deployment infrastructure for hosting agent endpoints. Enables building chatbots, task automation, and agentic workflows without managing agent orchestration infrastructure.","intents":["Build chatbots that can take actions (book appointments, query databases, send emails)","Create autonomous agents that complete multi-step tasks without human intervention","Implement conversational interfaces for customer support and internal tools","Deploy agents as API endpoints for integration into other applications"],"best_for":["Teams building customer support chatbots with action capabilities","Developers implementing autonomous workflow automation","SaaS platforms adding conversational AI features","Enterprises deploying AI agents for internal process automation"],"limitations":["Agent framework architecture and capabilities are undocumented","Memory and context management implementation details are undocumented","Tool calling schema format and validation are undocumented","No documented support for multi-agent orchestration or agent-to-agent communication","Conversation history persistence and retrieval are undocumented","Error handling and fallback mechanisms are undocumented","Pricing and usage limits for agents are undocumented"],"requires":["Vercel account","API keys for external services (LLM providers, databases, APIs)","Tool definitions and function implementations","Agents product access (availability unknown)"],"input_types":["User messages (text)","Tool definitions (schemas)","Function implementations","MCP server connections"],"output_types":["Agent responses (text)","Tool invocations (function calls)","Action results (structured data)","Conversation history"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vercel__cap_8","uri":"capability://tool.use.integration.mcp.server.support.for.ai.agent.tool.integration","name":"mcp server support for ai agent tool integration","description":"Native support for Model Context Protocol (MCP) servers, enabling AI agents to interact with external systems through standardized tool interfaces. Agents can invoke MCP server tools for database queries, API calls, file operations, and custom business logic. Eliminates need for custom tool adapters by using MCP's standardized protocol. Integrates with Vercel Agents and AI SDK for seamless tool calling.","intents":["Connect AI agents to databases and APIs using standardized MCP protocol","Implement custom tools for agents without writing adapter code","Share tool definitions across multiple agents and applications","Build reusable tool libraries that work with any MCP-compatible agent"],"best_for":["Teams building agents that need to interact with multiple external systems","Organizations standardizing on MCP for AI tool integration","Developers building reusable tool libraries for agents","Enterprises requiring standardized interfaces for agent-system interaction"],"limitations":["MCP server implementation details and supported server types are undocumented","Tool schema format and validation rules are undocumented","Error handling and timeout behavior for MCP calls are undocumented","No documented support for authentication/authorization in MCP servers","Performance characteristics (latency, throughput) of MCP calls are undocumented","No documented support for streaming responses from MCP tools"],"requires":["Vercel Agents or AI SDK","MCP server implementation (external or custom)","Tool schema definitions compatible with MCP protocol"],"input_types":["MCP server configuration","Tool invocation requests (JSON)","Tool parameters (structured data)"],"output_types":["Tool execution results (JSON)","Error responses","Structured data from external systems"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vercel__cap_9","uri":"capability://automation.workflow.sandbox.execution.environment.for.untrusted.code","name":"sandbox execution environment for untrusted code","description":"Provides isolated execution environment for running untrusted code safely. Sandboxes prevent code from accessing host system, other applications, or sensitive data. Enables building platforms that execute user-submitted code (coding challenges, AI-generated code, user scripts) without security risk. Integrates with Vercel's deployment infrastructure for seamless integration into applications.","intents":["Execute user-submitted code safely in coding challenge platforms","Run AI-generated code without trusting the output","Allow users to run custom scripts in SaaS applications","Build platforms that execute untrusted code without security risk"],"best_for":["Coding challenge and competitive programming platforms","AI-powered code generation tools requiring code execution","SaaS platforms allowing user-submitted scripts","Educational platforms teaching programming with live code execution"],"limitations":["Sandbox isolation guarantees and security model are undocumented","Supported languages and runtimes are undocumented","Execution timeout limits and resource constraints are undocumented","File system access and persistence are undocumented","Network access from sandboxed code is undocumented","Pricing and usage limits for sandbox execution are undocumented","No documented support for GPU or specialized hardware"],"requires":["Vercel account","Sandbox product access (availability unknown)","Code to execute (any supported language)"],"input_types":["Source code (language-dependent)","Input data for code execution","Environment variables","File system data"],"output_types":["Execution results (stdout, stderr)","Exit code","Execution time and resource usage","Error messages"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vercel__headline","uri":"capability://deployment.infra.frontend.cloud.platform.for.deploying.web.applications","name":"frontend cloud platform for deploying web applications","description":"Vercel is a leading frontend cloud platform that allows developers to deploy web applications with zero configuration, featuring edge functions, image optimization, and seamless integration with Next.js.","intents":["best frontend cloud platform","frontend platform for web applications","top deployment platform for Next.js","best platform for AI web applications","frontend hosting solutions for developers"],"best_for":["developers looking for easy deployment solutions","teams needing collaboration features"],"limitations":["may have cold start latency","bandwidth costs based on usage"],"requires":[],"input_types":[],"output_types":[],"categories":["deployment-infra"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":56,"verified":false,"data_access_risk":"high","permissions":["Git repository on GitHub, GitLab, or Bitbucket","Vercel account (free tier available)","Web application using supported framework (Next.js, Nuxt, Svelte, etc.)","Vercel account (Pro tier recommended for production workloads)","API key for external services (OpenAI, Anthropic, etc.) if using AI features","TypeScript or JavaScript runtime (Node.js 18+)","Vercel account","Custom domain ownership","DNS access to configure domain records","Feature flag definitions"],"failure_modes":["Automatic deployment on every push may not suit teams requiring manual approval gates","Build time limits and parallelization details are undocumented","Preview environments are ephemeral and tied to branch lifetime","No documented support for monorepo selective deployment (deploy only changed packages)","Cold start latency is not quantified; 'cold start prevention' is Pro-only feature with unknown performance impact","Function timeout limits and maximum concurrent execution limits are undocumented","No GPU support documented; Fluid Compute appears CPU-only","Edge request quotas may be insufficient for high-volume APIs (10M/month Pro = ~3.8K requests/hour average)","Regional pricing details are unknown; potential cost variance across regions","DNS provider integration details and supported providers are undocumented","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.25,"ecosystem":0.15,"match_graph":0.25,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:34.118Z","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=vercel","compare_url":"https://unfragile.ai/compare?artifact=vercel"}},"signature":"wh7ZO/LAlsPGVSn3h4uVPhrPhP+Y6Oqj1z9WzpJeXeKqHUW1EChsLMbuVnv8US/h+240jLBQzQyGBUPIZ97XDw==","signedAt":"2026-06-21T14:22:31.102Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/vercel","artifact":"https://unfragile.ai/vercel","verify":"https://unfragile.ai/api/v1/verify?slug=vercel","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"}}