{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-upstash--context7","slug":"upstash--context7","name":"context7","type":"product","url":"https://context7.com","page_url":"https://unfragile.ai/upstash--context7","categories":["documentation"],"tags":["llm","mcp","mcp-server","vibe-coding"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-upstash--context7__cap_0","uri":"capability://tool.use.integration.mcp.based.version.specific.documentation.retrieval.with.llm.powered.ranking","name":"mcp-based version-specific documentation retrieval with llm-powered ranking","description":"Implements a Model Context Protocol server that exposes documentation as callable tools for 30+ AI coding assistants (Cursor, Claude Code, VS Code Copilot, Windsurf). Uses an indexed, searchable documentation store with LLM-powered ranking to surface the most relevant library documentation snippets for a given query, preventing API hallucinations by grounding LLM responses in current, version-specific docs. The MCP transport layer abstracts away client-specific integration details, allowing a single server implementation to serve multiple AI editor ecosystems.","intents":["I want my AI coding assistant to have access to current library documentation without hallucinating outdated APIs","I need to integrate documentation retrieval into my AI agent without building separate connectors for each LLM platform","I want to ensure code generation respects the exact version of a library my project uses"],"best_for":["AI coding assistant developers building multi-client integrations","Teams using Cursor, Claude Code, or VS Code Copilot who need accurate library docs","LLM agent builders who want grounded documentation context without manual prompt engineering"],"limitations":["Requires pre-indexed library documentation in Context7's store — not all libraries are available","MCP transport adds latency for remote server configurations (typical 200-500ms per query)","LLM-powered ranking depends on embedding quality and may miss edge-case API usage patterns","Version resolution relies on library metadata availability — some private or niche packages may not be supported"],"requires":["MCP client support (Claude 3.5+, Cursor 0.42+, VS Code 1.80+ with Copilot extension)","Network connectivity for remote server at mcp.context7.com or local MCP server deployment","API key for authentication if using remote server or private library access"],"input_types":["natural language query (e.g., 'how do I use React hooks')","library name and optional version specifier (e.g., 'react@18.2.0')","code snippet context (optional, for semantic ranking)"],"output_types":["structured documentation snippets with source attribution","code examples with syntax highlighting","API reference data (function signatures, parameters, return types)","version-specific deprecation warnings"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-upstash--context7__cap_1","uri":"capability://data.processing.analysis.semantic.library.identification.and.resolution.with.auto.detection","name":"semantic library identification and resolution with auto-detection","description":"Implements the 'resolve-library-id' MCP tool that automatically identifies which libraries are referenced in code or natural language queries, then resolves them to canonical library identifiers in Context7's index. Uses pattern matching, import statement parsing, and semantic understanding to handle aliases, monorepo packages, and version specifiers. The tool bridges the 'Natural Language Space' of developer prompts to the 'Code Entity Space' of indexed libraries, enabling downstream documentation queries without explicit library name specification.","intents":["I want the AI assistant to automatically know which libraries I'm using without me spelling them out","I need to resolve package aliases (e.g., '@babel/core' vs 'babel') to the correct documentation","I want version-specific docs even when the developer just says 'React' without specifying 18 vs 19"],"best_for":["Developers working in polyglot codebases with many dependencies","Teams using monorepo structures where package names differ from import paths","AI agent builders who want automatic context enrichment without explicit library specification"],"limitations":["Requires library to be pre-registered in Context7's index — custom or private packages need manual registration","Alias resolution depends on pattern database — uncommon package naming conventions may fail","Version detection from code requires parseable import statements — dynamic requires or string-based imports may not resolve","Monorepo package resolution limited to known monorepo structures (React, Next.js, etc.)"],"requires":["Library must exist in Context7's indexed documentation store","Code context or explicit library name in query","For private libraries: library must be claimed/registered via Context7 admin panel"],"input_types":["import statements (ES6, CommonJS, TypeScript)","natural language library references (e.g., 'using React')","package.json dependencies","version specifiers (semver, ranges, exact versions)"],"output_types":["canonical library identifier (e.g., 'react')","resolved version (e.g., '18.2.0')","library metadata (npm registry link, documentation URL)","confidence score for resolution accuracy"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-upstash--context7__cap_10","uri":"capability://automation.workflow.dashboard.and.usage.analytics.with.teamspace.management","name":"dashboard and usage analytics with teamspace management","description":"Provides a web dashboard for monitoring Context7 usage, viewing query history, managing team access, and configuring library settings. Includes usage metrics (queries/month, libraries accessed, top queries), teamspace management (invite team members, set permissions), and library admin panel (claim libraries, manage documentation, view indexing status). Supports OAuth 2.0 for authentication and role-based access control (admin, editor, viewer). Analytics data is aggregated and anonymized for privacy.","intents":["I want to see how my team is using Context7 and which libraries are most accessed","I need to manage team access and permissions for Context7","I want to claim my library and manage its documentation in Context7"],"best_for":["Team leads and managers monitoring AI coding assistant usage","Library maintainers who want to manage their documentation in Context7","Enterprise teams with multiple users and complex access control needs"],"limitations":["Dashboard requires web browser access — no CLI alternative for analytics","Usage data aggregated hourly — real-time metrics not available","Teamspace management limited to Context7 — no integration with external identity providers (LDAP, SAML)","Library admin panel requires library ownership claim — no bulk management for multiple libraries","Analytics data retention limited to 90 days — long-term historical analysis not available"],"requires":["Context7 account with OAuth 2.0 authentication","Web browser with JavaScript enabled","Admin or editor role for teamspace management"],"input_types":["OAuth credentials for login","team member email for invitations","library name for claiming"],"output_types":["usage metrics (charts, tables)","query history with timestamps","team member list with roles","library indexing status and metadata"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-upstash--context7__cap_11","uri":"capability://automation.workflow.enterprise.deployment.with.on.premise.and.kubernetes.support","name":"enterprise deployment with on-premise and kubernetes support","description":"Provides enterprise-grade deployment options including on-premise Docker Compose setup, Kubernetes deployment with Helm charts, and managed cloud deployment. Supports private repository access for internal libraries, custom authentication (OAuth 2.0, LDAP, SAML), and data residency compliance (GDPR, HIPAA). Includes Docker Compose templates for single-server deployment and Kubernetes manifests for multi-node clusters. Enterprise plans include SLA guarantees, dedicated support, and custom rate limits.","intents":["I need to deploy Context7 on-premise for data residency or security reasons","I want to run Context7 in Kubernetes with high availability and auto-scaling","I need to integrate Context7 with my organization's identity provider (LDAP, SAML)"],"best_for":["Enterprise teams with strict data residency or security requirements","Organizations running Kubernetes infrastructure","Teams with custom authentication requirements (LDAP, SAML)","Companies needing SLA guarantees and dedicated support"],"limitations":["On-premise deployment requires infrastructure management — no managed service","Kubernetes deployment requires K8s expertise — not suitable for small teams","Custom authentication setup requires engineering effort — not plug-and-play","Enterprise plans significantly more expensive than cloud plans","On-premise deployment requires manual updates — no automatic security patches"],"requires":["Docker 20.10+ for Docker Compose deployment","Kubernetes 1.24+ for K8s deployment","Infrastructure to run containers (VMs, bare metal, or managed K8s service)","Enterprise plan subscription","API key or OAuth 2.0 setup for authentication"],"input_types":["Docker Compose configuration (docker-compose.yml)","Kubernetes manifests (YAML files)","authentication configuration (OAuth, LDAP, SAML)","library metadata for private repositories"],"output_types":["running Context7 server (local or K8s)","health check endpoints","metrics and logs (Prometheus, ELK compatible)","SLA compliance reports"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-upstash--context7__cap_12","uri":"capability://automation.workflow.github.actions.integration.for.automated.documentation.validation.in.ci.cd","name":"github actions integration for automated documentation validation in ci/cd","description":"Provides a GitHub Action that integrates Context7 into CI/CD pipelines for automated documentation validation. The action can query documentation for dependencies, validate generated code against official docs, and fail builds if documentation is outdated or unavailable. Supports matrix builds for testing against multiple library versions. Outputs validation results as GitHub check annotations and workflow artifacts. Can be combined with CodeRabbit integration for code review automation.","intents":["I want to validate that generated code matches the library documentation in my CI/CD pipeline","I need to fail builds if documentation for a dependency is outdated or missing","I want to test code generation against multiple library versions automatically"],"best_for":["Teams using GitHub Actions for CI/CD who want documentation validation","Library maintainers who want to ensure generated code matches their docs","Organizations building code generation tools that need documentation grounding"],"limitations":["GitHub Actions integration limited to GitHub repositories — no GitLab, Bitbucket support","Validation logic must be custom-written — no built-in validation rules","Action adds 30-60 seconds to CI/CD pipeline per run","Matrix builds multiply pipeline duration — not suitable for very large version matrices","Requires API key in GitHub secrets — key rotation requires manual updates"],"requires":["GitHub repository with Actions enabled","Context7 API key stored in GitHub secrets","GitHub Actions workflow file (YAML)"],"input_types":["library names and version ranges","code snippets to validate","custom validation rules (optional)"],"output_types":["GitHub check annotations with validation results","workflow artifacts with detailed reports","build pass/fail status"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-upstash--context7__cap_2","uri":"capability://search.retrieval.query.based.documentation.search.with.context.aware.ranking","name":"query-based documentation search with context-aware ranking","description":"Implements the 'query-docs' MCP tool that accepts natural language queries and returns ranked documentation snippets from the indexed library store. Uses semantic search (embeddings-based) combined with LLM-powered re-ranking to surface the most contextually relevant documentation. The ranking algorithm considers query intent, code context, library version, and documentation freshness. Results are returned with source attribution and version metadata, enabling LLMs to cite specific documentation sources.","intents":["I want to search library documentation semantically (e.g., 'how do I handle async operations') not just by keyword","I need documentation results ranked by relevance to my specific use case, not just keyword matches","I want to know which version of the library each documentation snippet applies to"],"best_for":["LLM-powered code generation systems that need grounded documentation context","AI agents building multi-step reasoning chains that require accurate API references","Developers using AI assistants who want to verify generated code against official docs"],"limitations":["Semantic search quality depends on embedding model — may miss domain-specific terminology","LLM-powered ranking adds 100-300ms latency per query compared to keyword search","Documentation must be pre-indexed — real-time docs (GitHub wikis, blog posts) not included","Ranking may favor popular patterns over edge-case or advanced usage","Query understanding limited to English — multilingual queries may degrade"],"requires":["Library documentation must be indexed in Context7 store","API key for authentication (rate limits: typically 100-1000 queries/month depending on plan)","Network connectivity to mcp.context7.com or local server"],"input_types":["natural language query (e.g., 'how to handle errors in async functions')","optional code context (snippet of code being written)","optional library version constraint","optional language/framework filter"],"output_types":["ranked list of documentation snippets (typically 3-10 results)","relevance scores (0-1 scale)","source attribution (library, version, doc section)","code examples with syntax highlighting","direct links to full documentation"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-upstash--context7__cap_3","uri":"capability://tool.use.integration.multi.client.mcp.server.with.transport.abstraction.and.remote.local.deployment","name":"multi-client mcp server with transport abstraction and remote/local deployment","description":"Provides a Model Context Protocol server implementation that abstracts away client-specific integration details, allowing a single codebase to serve Cursor, Claude Code, VS Code Copilot, Windsurf, and other MCP-compatible clients. Supports both remote deployment (at mcp.context7.com) and local deployment (Docker, Kubernetes, on-premise). The transport layer handles stdio, HTTP, and WebSocket protocols transparently. Configuration is client-specific (via ctx7 CLI setup command or manual config files), but the core MCP tool definitions remain consistent across all clients.","intents":["I want to deploy documentation retrieval once and have it work across all my team's AI coding assistants","I need to run Context7 locally for security/privacy reasons without rebuilding for each client","I want to support multiple AI editor ecosystems without maintaining separate integrations"],"best_for":["Enterprise teams using multiple AI coding assistants (Cursor + Claude Code + Copilot)","Organizations with strict data residency requirements needing on-premise deployment","Platform teams building AI coding assistant infrastructure"],"limitations":["MCP protocol overhead adds ~50-200ms latency per tool call vs direct API calls","Remote server deployment at mcp.context7.com requires internet connectivity","Local deployment requires Docker/Kubernetes expertise and infrastructure management","Client-specific setup still required (each editor needs separate config) — no universal configuration","Rate limiting applied per client/API key — high-volume usage may require enterprise plan"],"requires":["Node.js 18+ for local server deployment","Docker 20.10+ for containerized deployment","Kubernetes 1.24+ for K8s deployment (enterprise only)","MCP-compatible client (Cursor 0.42+, Claude Code, VS Code 1.80+, Windsurf)","API key for remote server or OAuth 2.0 setup for local deployment"],"input_types":["MCP tool call requests (resolve-library-id, query-docs)","client configuration (stdio, HTTP, WebSocket transport)","authentication credentials (API key or OAuth token)"],"output_types":["MCP tool responses (documentation snippets, library metadata)","server status and health metrics","usage analytics (queries/month, libraries accessed)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-upstash--context7__cap_4","uri":"capability://data.processing.analysis.library.indexing.and.documentation.ingestion.with.version.tracking","name":"library indexing and documentation ingestion with version tracking","description":"Implements a documentation ingestion pipeline that crawls library documentation (from npm, GitHub, official docs sites), parses it into semantic chunks, generates embeddings, and stores them with version metadata. The system maintains a searchable index of 1000+ libraries with version-specific documentation. Supports manual library registration via the Context7 admin panel for private or custom packages. The indexing process includes deduplication, freshness tracking, and LLM-powered summarization of documentation sections for improved ranking.","intents":["I want to add my custom library's documentation to Context7 so AI assistants can use it","I need to ensure documentation is kept up-to-date as my library releases new versions","I want to index documentation from multiple sources (npm, GitHub, custom docs site)"],"best_for":["Library maintainers who want their docs accessible to AI coding assistants","Enterprise teams with custom internal libraries needing documentation indexing","Platform teams managing documentation for multiple libraries"],"limitations":["Automatic indexing limited to public npm packages and GitHub repositories — private docs require manual upload","Indexing latency: 24-48 hours for new library versions to appear in search","Documentation parsing may fail for non-standard doc formats (custom HTML, proprietary formats)","Embedding quality depends on documentation clarity — poorly written docs rank lower","Version tracking limited to semver — non-standard versioning schemes may not resolve correctly"],"requires":["Library must be published to npm or have public GitHub repository","For private libraries: manual registration via Context7 admin panel with documentation upload","Documentation in standard formats (Markdown, HTML, JSDoc comments)","Library maintainer account or admin access to claim library"],"input_types":["npm package name and version range","GitHub repository URL","documentation files (Markdown, HTML, JSDoc)","library metadata (package.json, README)"],"output_types":["indexed documentation store with embeddings","version-specific documentation metadata","searchable documentation snippets","library admin dashboard with indexing status"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-upstash--context7__cap_5","uri":"capability://tool.use.integration.rest.api.for.programmatic.documentation.access.with.rate.limiting.and.authentication","name":"rest api for programmatic documentation access with rate limiting and authentication","description":"Exposes Context7's documentation retrieval capabilities via REST endpoints at https://mcp.context7.com/mcp, enabling programmatic access from non-MCP clients (scripts, CI/CD pipelines, custom integrations). Implements OAuth 2.0 and API key authentication with per-plan rate limits (typically 100-1000 queries/month). Endpoints include search-libraries (find available libraries), get-context (retrieve documentation for a library), and add-library (register custom libraries). Responses are JSON-formatted with version metadata and source attribution.","intents":["I want to query Context7 documentation from a custom script or CI/CD pipeline without using MCP","I need to build a custom AI agent that uses Context7 as a documentation backend","I want to integrate documentation retrieval into my own tooling without depending on MCP clients"],"best_for":["Custom AI agent builders who need documentation APIs","CI/CD pipeline developers who want to validate code against library docs","Teams building custom IDE plugins or editor extensions","Developers integrating Context7 into non-MCP environments (Python, Go, Java)"],"limitations":["Rate limits enforced per API key — high-volume usage requires enterprise plan","REST API latency typically 200-500ms per request (vs MCP's 50-200ms)","No streaming responses — large documentation sets returned as complete JSON","Authentication requires API key management — no built-in session management","Requires internet connectivity — no offline mode for REST API"],"requires":["API key (obtained from Context7 dashboard after signup)","HTTP client library (curl, fetch, requests, etc.)","Network connectivity to mcp.context7.com"],"input_types":["library name (e.g., 'react')","query string (e.g., 'how to use hooks')","version specifier (optional, e.g., '18.2.0')","API key in Authorization header"],"output_types":["JSON response with documentation snippets","library metadata (name, version, npm link)","relevance scores and source attribution","HTTP status codes and error messages"],"categories":["tool-use-integration","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-upstash--context7__cap_6","uri":"capability://tool.use.integration.cli.tool.ctx7.for.documentation.queries.and.library.management","name":"cli tool (ctx7) for documentation queries and library management","description":"Provides a command-line interface for querying documentation, managing libraries, and configuring Context7 integration. Commands include 'ctx7 docs' for querying documentation, 'ctx7 add-library' for registering custom libraries, 'ctx7 auth' for authentication setup, and 'ctx7 setup' for MCP client configuration. The CLI uses the TypeScript SDK internally and outputs results in human-readable format (Markdown, JSON). Supports shell completion and integration with shell scripts and CI/CD pipelines.","intents":["I want to query library documentation from the command line without opening an editor","I need to register my custom library with Context7 from a CI/CD pipeline","I want to set up Context7 integration with a single command"],"best_for":["Developers who prefer CLI workflows","CI/CD pipeline builders who need documentation validation","DevOps teams managing library documentation at scale","Shell script developers integrating documentation into automation"],"limitations":["CLI output is text-based — no interactive UI for browsing documentation","Requires Node.js installation — not available as standalone binary for all platforms","Shell completion setup required per shell (bash, zsh, fish) — not automatic","No built-in paging for large documentation results — requires piping to 'less' or similar","Authentication requires API key in environment variable or config file — less secure than OAuth in interactive clients"],"requires":["Node.js 18+ with npm","API key for authentication (set via CTX7_API_KEY environment variable or config file)","Bash, zsh, or other POSIX shell for shell completion"],"input_types":["command name (docs, add-library, auth, setup)","library name and optional version","query string for documentation search","library metadata for registration"],"output_types":["Markdown-formatted documentation snippets","JSON output (with --json flag)","CLI status messages and error messages","shell completion scripts"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-upstash--context7__cap_7","uri":"capability://tool.use.integration.typescript.sdk.for.direct.programmatic.integration","name":"typescript sdk for direct programmatic integration","description":"Provides @upstash/context7-sdk and @upstash/context7-tools-ai-sdk npm packages for direct TypeScript/JavaScript integration without MCP. The SDK exposes methods for querying documentation (query-docs), resolving libraries (resolve-library-id), and managing libraries. Supports both Node.js and browser environments. The AI SDK package includes integration with Vercel's AI SDK, enabling use in AI agent frameworks. SDK handles authentication, error handling, and response parsing internally.","intents":["I want to use Context7 in my TypeScript/JavaScript project without MCP infrastructure","I need to build an AI agent using Vercel's AI SDK with Context7 documentation backend","I want type-safe documentation queries with TypeScript interfaces"],"best_for":["TypeScript/JavaScript developers building custom AI agents","Teams using Vercel's AI SDK who want documentation integration","Node.js backend developers building documentation-aware services","Browser-based AI applications needing documentation context"],"limitations":["TypeScript only — no Python, Go, or other language SDKs","Browser usage requires CORS-enabled API endpoint — may not work with all deployment configurations","SDK adds ~50KB to bundle size (minified)","Error handling depends on API response format — breaking API changes may require SDK updates","No built-in caching — repeated queries hit the API each time"],"requires":["Node.js 18+ or modern browser with fetch API","npm or yarn package manager","API key for authentication","TypeScript 4.5+ (for type definitions)"],"input_types":["library name (string)","query string (string)","version specifier (optional, string)","API key (string, from environment or constructor)"],"output_types":["TypeScript interfaces for documentation snippets","Promise-based async responses","typed error objects with error codes"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-upstash--context7__cap_8","uri":"capability://tool.use.integration.editor.plugin.ecosystem.cursor.claude.code.vs.code.with.auto.invoke.rules","name":"editor plugin ecosystem (cursor, claude code, vs code) with auto-invoke rules","description":"Provides native plugins for Cursor, Claude Code, and VS Code that integrate Context7 documentation retrieval directly into editor workflows. Plugins expose documentation queries through editor commands, context menus, and auto-invoke rules (rules that automatically trigger documentation retrieval based on code patterns). Cursor and Claude Code plugins include 'Skills' (reusable documentation-aware prompts) and 'Agents' (multi-step documentation workflows). VS Code plugin uses rules and skills for configuration. Auto-invoke rules enable automatic documentation retrieval when developers mention specific libraries or write code patterns.","intents":["I want documentation to appear automatically when I mention a library in my code","I need to create reusable documentation-aware prompts (Skills) for my team","I want to build multi-step documentation workflows (Agents) in my editor"],"best_for":["Cursor and Claude Code users who want seamless documentation integration","VS Code Copilot users who need documentation context","Teams building custom documentation-aware coding workflows","Developers who want automatic documentation retrieval without manual queries"],"limitations":["Cursor and Claude Code plugins require specific editor versions (Cursor 0.42+, Claude Code latest)","VS Code plugin requires VS Code 1.80+ with Copilot extension","Auto-invoke rules limited to pattern matching — complex logic requires manual triggering","Skills and Agents are editor-specific — not portable across editors","Plugin updates require editor restart — no hot-reload capability"],"requires":["Cursor 0.42+ or Claude Code (latest) or VS Code 1.80+ with Copilot","Context7 API key for authentication","Editor configuration file (cursor.json, claude.json, or VS Code settings.json)"],"input_types":["code context (current file, selection)","natural language prompts (in editor chat)","library names and version specifiers","auto-invoke rule patterns"],"output_types":["documentation snippets in editor chat","code examples with syntax highlighting","inline documentation hints","multi-step agent responses"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-upstash--context7__cap_9","uri":"capability://planning.reasoning.docs.researcher.agent.with.automatic.library.identification.and.documentation.retrieval","name":"docs researcher agent with automatic library identification and documentation retrieval","description":"Implements an autonomous agent that automatically identifies libraries in code or prompts, retrieves relevant documentation, and synthesizes answers to developer questions. The agent uses a multi-step reasoning loop: identify libraries (via resolve-library-id), query documentation (via query-docs), rank results by relevance, and generate explanations grounded in official docs. Supports auto-invoke rules that trigger the agent when developers mention specific libraries or write code patterns. The agent maintains context across multiple turns, enabling follow-up questions about documentation.","intents":["I want an AI agent that automatically finds and explains library documentation without me specifying the library","I need multi-step reasoning that identifies libraries, retrieves docs, and synthesizes answers","I want documentation-grounded answers that cite specific sources"],"best_for":["Developers using Cursor or Claude Code who want automatic documentation context","Teams building custom AI agents that need documentation grounding","LLM application developers who want to reduce hallucinations via documentation"],"limitations":["Agent reasoning adds 500ms-2s latency per query (vs direct documentation retrieval)","Library identification may fail for uncommon or private packages — requires manual specification","Multi-turn context limited to current session — no persistent memory across sessions","Agent may over-fetch documentation for simple queries — not optimized for latency-sensitive use cases","Reasoning quality depends on LLM model — may produce incorrect conclusions despite accurate documentation"],"requires":["Cursor or Claude Code with Context7 plugin installed","API key for Context7 and LLM provider (OpenAI, Anthropic, etc.)","Code context or explicit library mention in prompt"],"input_types":["natural language question (e.g., 'how do I use React hooks')","code snippet with library imports","follow-up questions in multi-turn conversation"],"output_types":["synthesized explanation grounded in documentation","code examples from official docs","source attribution (library, version, doc section)","confidence scores for answers"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":37,"verified":false,"data_access_risk":"high","permissions":["MCP client support (Claude 3.5+, Cursor 0.42+, VS Code 1.80+ with Copilot extension)","Network connectivity for remote server at mcp.context7.com or local MCP server deployment","API key for authentication if using remote server or private library access","Library must exist in Context7's indexed documentation store","Code context or explicit library name in query","For private libraries: library must be claimed/registered via Context7 admin panel","Context7 account with OAuth 2.0 authentication","Web browser with JavaScript enabled","Admin or editor role for teamspace management","Docker 20.10+ for Docker Compose deployment"],"failure_modes":["Requires pre-indexed library documentation in Context7's store — not all libraries are available","MCP transport adds latency for remote server configurations (typical 200-500ms per query)","LLM-powered ranking depends on embedding quality and may miss edge-case API usage patterns","Version resolution relies on library metadata availability — some private or niche packages may not be supported","Requires library to be pre-registered in Context7's index — custom or private packages need manual registration","Alias resolution depends on pattern database — uncommon package naming conventions may fail","Version detection from code requires parseable import statements — dynamic requires or string-based imports may not resolve","Monorepo package resolution limited to known monorepo structures (React, Next.js, etc.)","Dashboard requires web browser access — no CLI alternative for analytics","Usage data aggregated hourly — real-time metrics not available","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.4342122279834571,"quality":0.35,"ecosystem":0.52,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.064Z","last_scraped_at":"2026-05-03T13:56:56.344Z","last_commit":"2026-05-03T01:34:06Z"},"community":{"stars":54340,"forks":2583,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=upstash--context7","compare_url":"https://unfragile.ai/compare?artifact=upstash--context7"}},"signature":"Us7rXT5ag+BC9f8Xd752/UZ/I9ItntRgj2d6miyDVXbgToAQ5hLoIIP2xubCfj28KkwI8uboNPmQDsvLhbsbAA==","signedAt":"2026-06-20T21:15:11.088Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/upstash--context7","artifact":"https://unfragile.ai/upstash--context7","verify":"https://unfragile.ai/api/v1/verify?slug=upstash--context7","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"}}