{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github_mcp-upstash-context7","slug":"mcp-upstash-context7","name":"context7","type":"mcp","url":"https://github.com/upstash/context7","page_url":"https://unfragile.ai/mcp-upstash-context7","categories":["mcp-servers"],"tags":["llm","mcp","mcp-server","vibe-coding"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github_mcp-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":"Exposes documentation for 30+ library versions through the Model Context Protocol (MCP) standard, implementing a two-tool system (resolve-library-id and query-docs) that maps natural language library references to specific versions and retrieves ranked, semantically-relevant documentation snippets. The system uses LLM-powered ranking to surface the most contextually relevant documentation sections rather than simple keyword matching, enabling AI assistants to access current API signatures and examples without hallucination.","intents":["I want my AI coding assistant to always have access to the latest library documentation for the exact version I'm using","I need to prevent my LLM from generating code using deprecated APIs or outdated patterns","I want to query documentation programmatically from Claude, Cursor, or other AI editors without manual context switching"],"best_for":["AI coding assistant developers integrating documentation into their platforms","Teams using Cursor, Claude Code, VS Code Copilot, or Windsurf who need version-accurate code generation","Enterprise development teams managing multiple library versions across codebases"],"limitations":["Requires pre-indexed library documentation in Context7's store — custom or private libraries need manual addition via API","LLM-powered ranking adds ~100-200ms latency per query compared to simple keyword search","MCP transport layer adds protocol overhead; local server deployment recommended for sub-100ms latency requirements","Documentation freshness depends on Context7's indexing schedule — may lag behind latest library releases by hours to days"],"requires":["MCP-compatible client (Cursor, Claude Code, VS Code, Windsurf, or custom MCP client)","API key for Context7 (free tier available, rate limits apply)","Network connectivity to mcp.context7.com OR local MCP server deployment","TypeScript/Node.js 18+ for local server setup"],"input_types":["natural language library name (e.g., 'React', 'lodash')","version specifier (e.g., '18.2.0', 'latest', 'v3')","natural language query about library functionality"],"output_types":["structured documentation snippets with source attribution","ranked code examples with API signatures","library metadata (version, description, repository links)"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-upstash-context7__cap_1","uri":"capability://tool.use.integration.automatic.library.identification.and.version.resolution.from.code.context","name":"automatic library identification and version resolution from code context","description":"The resolve-library-id MCP tool automatically maps natural language library references (e.g., 'React', 'the HTTP client I'm using') to specific library identifiers and versions by analyzing the developer's codebase context and project dependencies. This capability eliminates the need for explicit version specification by examining package.json, import statements, and AI editor context to infer which version the developer is actually using.","intents":["I want the AI assistant to automatically know which version of React/Next.js/etc. I'm using without me specifying it","I need the documentation tool to resolve ambiguous library names (e.g., 'axios' vs 'fetch') based on what's actually in my project","I want version-specific docs without manually managing version numbers in my prompts"],"best_for":["Developers working in Cursor or Claude Code with integrated project context","Teams with heterogeneous library versions across multiple projects","Developers who want zero-friction documentation lookup without version management overhead"],"limitations":["Requires access to project dependency files (package.json, requirements.txt, etc.) — fails silently for projects without explicit dependency declarations","Cannot resolve versions for dynamically-loaded or runtime-installed libraries","Ambiguous library names may resolve to incorrect version if multiple candidates exist in dependencies","Monorepo projects with multiple versions of the same library may resolve to the wrong version without additional context"],"requires":["Project with explicit dependency manifest (package.json, pyproject.toml, Gemfile, etc.)","MCP client with codebase context access (Cursor, Claude Code, or similar)","Library must be indexed in Context7's documentation store"],"input_types":["natural language library reference (string)","codebase context (file paths, imports, dependencies)"],"output_types":["resolved library identifier (string)","detected version (semantic version string)","confidence score (0-1)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-upstash-context7__cap_10","uri":"capability://data.processing.analysis.library.indexing.and.documentation.ingestion.pipeline.with.version.tracking","name":"library indexing and documentation ingestion pipeline with version tracking","description":"Context7 provides APIs and workflows for adding custom libraries to its documentation index, including automatic documentation parsing, version tracking, and indexing for semantic search. The system supports adding libraries via REST API endpoints, CLI commands, or web dashboard, with support for multiple documentation formats (Markdown, HTML, JSDoc) and automatic version detection from package manifests.","intents":["I want to add my private or custom library to Context7 so it's available for documentation lookup","I need to keep my library's documentation up-to-date in Context7 as new versions are released","I want to index internal documentation or proprietary APIs for use with AI assistants"],"best_for":["Teams with private or custom libraries that need documentation indexing","Organizations managing internal frameworks or SDKs","Enterprise teams who want to index proprietary documentation for AI assistants"],"limitations":["Library indexing requires manual API calls or CLI invocation; no automatic CI/CD integration out-of-the-box","Documentation parsing supports limited formats (Markdown, HTML, JSDoc); custom formats require preprocessing","Indexing latency varies (minutes to hours) depending on documentation size","No built-in versioning strategy; teams must manage version numbering and release coordination","Indexed documentation is stored in Context7's infrastructure; no option for private/on-premise storage in free tier"],"requires":["API key for Context7 with library management permissions","Documentation in supported format (Markdown, HTML, JSDoc, or similar)","Library metadata (name, version, description, repository URL)","Network connectivity to Context7 API"],"input_types":["library metadata (name, version, description)","documentation content (Markdown, HTML, or JSDoc)","version specifier (semantic version string)"],"output_types":["library identifier (string)","indexing status (success/failure)","indexed documentation metadata"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-upstash-context7__cap_11","uri":"capability://automation.workflow.dashboard.and.usage.analytics.with.teamspace.management.and.billing","name":"dashboard and usage analytics with teamspace management and billing","description":"Context7 provides a web dashboard for managing libraries, viewing usage metrics, configuring teamspaces, and managing billing. The dashboard displays documentation lookup statistics, API usage, team member access, and library management controls, enabling teams to monitor documentation usage patterns and manage access across multiple developers.","intents":["I want to see how much my team is using the documentation service and which libraries are most queried","I need to manage team access and permissions for Context7 across multiple developers","I want to track API usage and billing for my organization's documentation service"],"best_for":["Team leads and engineering managers monitoring documentation usage","Organizations managing billing and access control for shared services","Teams tracking which libraries are most used by their developers"],"limitations":["Dashboard is web-based; requires browser access and internet connectivity","Analytics are aggregated; no per-developer or per-project breakdown in free tier","Billing integration requires manual setup and payment method configuration","No API for programmatic access to analytics; dashboard is UI-only"],"requires":["Context7 account (free or paid tier)","Web browser with internet connectivity","Admin or team lead permissions in Context7 account"],"input_types":["user interactions in web dashboard","API usage data (collected automatically)"],"output_types":["usage metrics and analytics (charts, tables)","team member list and permissions","billing information and invoices"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-upstash-context7__cap_12","uri":"capability://automation.workflow.enterprise.on.premise.deployment.with.docker.compose.and.kubernetes.support","name":"enterprise on-premise deployment with docker compose and kubernetes support","description":"Context7 supports enterprise on-premise deployment via Docker Compose and Kubernetes, enabling organizations to run the entire documentation service within their own infrastructure. The deployment includes support for private documentation storage, custom authentication (OAuth 2.0, SAML), and teamspace policies for managing access across departments.","intents":["I need to deploy Context7 within our organization's infrastructure for compliance or security reasons","I want to run documentation service on-premise without sending data to external servers","I need custom authentication (OAuth, SAML) and access control for our enterprise deployment"],"best_for":["Enterprise organizations with strict data residency or compliance requirements","Teams with sensitive proprietary documentation that cannot be stored externally","Organizations with existing Kubernetes infrastructure who want to integrate documentation service"],"limitations":["Requires significant infrastructure management (Docker, Kubernetes, networking, storage)","On-premise deployment requires dedicated DevOps resources for maintenance and updates","No automatic updates; teams must manually manage version upgrades","Requires enterprise license; significantly more expensive than cloud tier","Documentation index must be manually synced or mirrored from public Context7 service"],"requires":["Docker and Docker Compose OR Kubernetes cluster","Enterprise license for Context7","Dedicated infrastructure (servers, storage, networking)","DevOps expertise for deployment and maintenance","Custom authentication infrastructure (OAuth, SAML, LDAP)"],"input_types":["Docker Compose configuration files","Kubernetes manifests and Helm charts","authentication configuration (OAuth, SAML)"],"output_types":["running Context7 service within organization's infrastructure","documentation index stored on-premise","audit logs and access control"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-upstash-context7__cap_13","uri":"capability://planning.reasoning.docs.researcher.agent.for.autonomous.documentation.discovery.and.context.injection","name":"docs researcher agent for autonomous documentation discovery and context injection","description":"Context7 provides a Docs Researcher Agent that autonomously discovers and fetches relevant documentation based on developer queries or code context, automatically injecting documentation into the AI assistant's context without explicit user invocation. The agent uses auto-invoke rules to detect when documentation might be relevant and proactively fetches it, reducing the need for manual documentation lookup.","intents":["I want the AI assistant to automatically fetch relevant documentation without me explicitly asking for it","I need the agent to understand when documentation is relevant based on my code or query","I want documentation to be proactively injected into the chat context as I work"],"best_for":["Developers who want automatic documentation discovery without explicit prompting","Teams using AI assistants that support agent-based documentation research","Developers who want to reduce manual documentation lookup overhead"],"limitations":["Agent-based auto-invoke may add latency to chat responses (100-500ms) while fetching documentation","Auto-invoke rules can be overly broad, triggering unnecessary documentation fetches","Agent may fetch irrelevant documentation if it misunderstands the developer's intent","Requires careful tuning of auto-invoke rules to avoid excessive documentation injection"],"requires":["AI editor with agent support (Cursor, Claude Code, or similar)","Context7 plugin or MCP server configured with auto-invoke rules","API key for Context7"],"input_types":["developer queries or code context","auto-invoke rule patterns (regex or keyword-based)"],"output_types":["automatically fetched documentation injected into chat context","documentation snippets with source attribution"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-upstash-context7__cap_2","uri":"capability://tool.use.integration.multi.client.mcp.server.with.standardized.tool.interface.across.30.ai.editors","name":"multi-client mcp server with standardized tool interface across 30+ ai editors","description":"Context7 implements the Model Context Protocol (MCP) specification to expose documentation tools through a standardized interface that works across 30+ AI coding assistants (Cursor, Claude Code, VS Code Copilot, Windsurf, etc.) without requiring separate integrations for each client. The MCP server exposes tools via stdio, HTTP, or SSE transports, allowing clients to discover and invoke documentation retrieval with consistent schemas and error handling.","intents":["I want to use the same documentation tool across multiple AI editors without reconfiguring for each one","I need a standardized way to integrate documentation retrieval into any MCP-compatible AI assistant","I want to deploy a single documentation server that works with Cursor, Claude Code, and other editors simultaneously"],"best_for":["Teams using multiple AI coding assistants (Cursor + Claude Code + VS Code Copilot)","MCP server developers building documentation integrations for multiple clients","Enterprise deployments requiring a single source of truth for documentation across tools"],"limitations":["MCP protocol overhead adds ~50-100ms latency compared to direct API calls","Client-side MCP implementation quality varies — some editors may have bugs or incomplete MCP support","Tool discovery and schema negotiation requires MCP-compliant client; non-MCP tools cannot access the server","Transport layer (stdio vs HTTP vs SSE) must be configured per client, adding deployment complexity"],"requires":["MCP-compatible AI editor (Cursor 0.40+, Claude Code, VS Code 1.80+ with MCP extension, Windsurf, etc.)","MCP server running locally or remotely (Node.js 18+ for local deployment)","Network connectivity between client and MCP server (local or remote)"],"input_types":["MCP tool invocation requests (JSON-RPC 2.0 format)","tool parameters (library name, query, version)"],"output_types":["MCP tool results (JSON-structured documentation, metadata)","MCP error responses with standardized error codes"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-upstash-context7__cap_3","uri":"capability://search.retrieval.semantic.documentation.search.with.version.aware.ranking.and.context.filtering","name":"semantic documentation search with version-aware ranking and context filtering","description":"The query-docs MCP tool implements semantic search over indexed library documentation using LLM-powered ranking that understands developer intent and filters results by library version. Rather than keyword matching, the system uses embeddings and LLM-based relevance scoring to surface documentation sections that are semantically related to the developer's query, with results ranked by relevance to the specific library version being used.","intents":["I want to search library documentation semantically (e.g., 'how do I handle async operations' returns relevant docs even if my query doesn't match exact API names)","I need documentation results filtered to my specific library version, not a mix of versions","I want the most relevant documentation snippet ranked first, not just keyword matches"],"best_for":["Developers using AI assistants to understand library APIs without reading full documentation","Teams working with libraries that have large, complex documentation (React, Django, Kubernetes)","Developers who want semantic understanding of documentation rather than keyword-based search"],"limitations":["Semantic ranking adds 100-200ms latency per query compared to keyword search","LLM-powered ranking can hallucinate or misunderstand context in edge cases","Documentation quality directly impacts search results — poorly-written or incomplete docs rank poorly","Embeddings and ranking models are trained on general text; domain-specific jargon may not rank optimally","No support for cross-version documentation comparison or migration guides"],"requires":["Library documentation pre-indexed in Context7's store with embeddings computed","API key for Context7 (rate limits apply based on tier)","Network connectivity to Context7's ranking service (mcp.context7.com)"],"input_types":["natural language query (string, e.g., 'how to handle errors in async functions')","library identifier (string)","version specifier (string, optional)"],"output_types":["ranked list of documentation snippets (text with source attribution)","relevance scores (0-1)","source metadata (section title, URL, version)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-upstash-context7__cap_4","uri":"capability://automation.workflow.cli.based.documentation.management.and.skill.generation.ctx7.command","name":"cli-based documentation management and skill generation (ctx7 command)","description":"The ctx7 CLI tool provides developers with command-line access to documentation lookup, library management, and skill generation workflows. It enables local documentation queries without requiring an AI editor, supports adding custom libraries to the Context7 index, manages authentication tokens, and generates reusable 'skills' (documentation snapshots) that can be shared across teams or embedded in prompts.","intents":["I want to query documentation from the command line without opening an AI editor","I need to add my private or custom library to Context7's documentation index","I want to generate a reusable documentation snapshot ('skill') that I can share with my team or use in multiple prompts"],"best_for":["Developers who prefer CLI workflows over GUI-based tools","Teams managing custom or private libraries that need documentation indexing","DevOps and infrastructure teams integrating documentation lookup into CI/CD pipelines","Developers generating documentation snapshots for prompt engineering or team sharing"],"limitations":["CLI interface is less discoverable than IDE integration — requires knowledge of available commands","Skill generation requires manual invocation; no automatic skill updates when library documentation changes","Private library addition requires API key and manual configuration; no automatic dependency detection","CLI output is text-based; no interactive UI for browsing or filtering results"],"requires":["Node.js 18+ and npm/yarn","ctx7 CLI installed globally or locally (npm install -g @upstash/context7-cli)","API key for Context7 (for private library management and authentication)","Internet connectivity to Context7 API"],"input_types":["command-line arguments (library name, query, version)","library metadata (for adding custom libraries)","authentication token (API key)"],"output_types":["text-formatted documentation snippets","JSON-structured results (with --json flag)","skill files (YAML or JSON format for reuse)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-upstash-context7__cap_5","uri":"capability://tool.use.integration.cursor.and.claude.code.plugin.integration.with.auto.invoke.rules.and.custom.skills","name":"cursor and claude code plugin integration with auto-invoke rules and custom skills","description":"Context7 provides native plugins for Cursor and Claude Code that integrate documentation retrieval directly into the editor's UI and chat interface. The plugins support auto-invoke rules (automatically trigger documentation lookup based on patterns like '@library-name' mentions), custom skills (reusable documentation snapshots), and agent-based documentation research that can autonomously fetch relevant docs without explicit user invocation.","intents":["I want documentation to automatically appear in my Cursor/Claude Code chat when I mention a library name","I need to create custom documentation 'skills' that my team can reuse across projects","I want the AI assistant to autonomously research and fetch documentation without me explicitly asking for it"],"best_for":["Cursor and Claude Code users who want seamless documentation integration in their editor","Teams creating reusable documentation skills for internal libraries or frameworks","Developers who want automatic documentation lookup without explicit prompting"],"limitations":["Plugins are editor-specific; Cursor plugin doesn't work in Claude Code and vice versa","Auto-invoke rules require pattern configuration; overly broad patterns may trigger unnecessary documentation lookups","Custom skills require manual creation and maintenance; no automatic skill generation from library changes","Agent-based auto-invoke may add latency to chat responses if documentation fetching is slow"],"requires":["Cursor 0.40+ or Claude Code (latest version)","Context7 plugin installed from editor's plugin marketplace","API key for Context7 (free tier available)","Project with recognized library dependencies for auto-invoke to work"],"input_types":["natural language chat messages in editor","library mentions (e.g., '@react', '@lodash')","custom skill definitions (YAML or JSON)"],"output_types":["documentation snippets injected into chat context","auto-invoked documentation in editor sidebar","skill files for team sharing"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-upstash-context7__cap_6","uri":"capability://tool.use.integration.typescript.sdk.for.programmatic.documentation.access.and.integration","name":"typescript sdk for programmatic documentation access and integration","description":"Context7 provides a TypeScript SDK (@upstash/context7-sdk) that enables developers to programmatically query documentation, manage libraries, and integrate documentation retrieval into custom applications, agents, or workflows. The SDK wraps the REST API with type-safe methods, supports both Node.js and browser environments, and includes utilities for embedding documentation in LLM prompts.","intents":["I want to build a custom agent or application that queries documentation programmatically","I need type-safe TypeScript bindings for the Context7 API","I want to integrate documentation retrieval into my LLM application without using MCP"],"best_for":["TypeScript/Node.js developers building custom LLM agents or applications","Teams building internal tools that need documentation integration","Developers who prefer programmatic API access over MCP or CLI interfaces"],"limitations":["TypeScript-only; no Python, Go, or other language SDKs available","Requires API key management and authentication in application code","No built-in caching; applications must implement their own caching layer for repeated queries","Browser environment support is limited by CORS and authentication constraints"],"requires":["TypeScript 4.5+ or JavaScript (Node.js 18+)","npm/yarn package manager","API key for Context7","@upstash/context7-sdk package installed"],"input_types":["library identifier (string)","version specifier (string)","query string (natural language)"],"output_types":["typed documentation results (TypeScript interfaces)","library metadata (version, description, links)","structured code examples"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-upstash-context7__cap_7","uri":"capability://tool.use.integration.ai.sdk.integration.for.vercel.ai.sdk.and.custom.tool.definitions","name":"ai sdk integration for vercel ai sdk and custom tool definitions","description":"Context7 provides integration with Vercel's AI SDK through the @upstash/context7-tools-ai-sdk package, enabling developers to define documentation retrieval as a tool within AI SDK-based applications. The integration automatically handles tool schema generation, parameter validation, and result formatting, allowing documentation lookup to be seamlessly integrated into AI SDK's tool-calling workflows.","intents":["I'm using Vercel AI SDK and want to add documentation retrieval as a tool for my LLM agent","I need automatic tool schema generation for documentation queries in my AI SDK application","I want to integrate documentation lookup into my AI SDK agent without manual tool definition"],"best_for":["Developers building LLM agents with Vercel AI SDK","Teams using AI SDK's tool-calling framework who need documentation integration","Applications that want documentation as a first-class tool in their agent toolset"],"limitations":["Requires Vercel AI SDK; not compatible with other LLM frameworks (LangChain, LlamaIndex, etc.)","Tool schema is fixed by Context7; limited customization of tool parameters or output format","Adds dependency on @upstash/context7-tools-ai-sdk package; increases bundle size"],"requires":["Vercel AI SDK installed (npm install ai)","@upstash/context7-tools-ai-sdk package","API key for Context7","TypeScript 4.5+ or Node.js 18+"],"input_types":["AI SDK tool invocation parameters (library name, query, version)","LLM-generated tool calls"],"output_types":["documentation results formatted for AI SDK","tool execution results (JSON)"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-upstash-context7__cap_8","uri":"capability://automation.workflow.remote.mcp.server.deployment.with.http.sse.transport.and.authentication","name":"remote mcp server deployment with http/sse transport and authentication","description":"Context7 supports remote MCP server deployment via HTTP and Server-Sent Events (SSE) transports, enabling clients to connect to a centralized documentation server without running a local instance. The deployment includes built-in authentication (API key validation), rate limiting, and support for Docker and Kubernetes deployments, allowing teams to manage a single shared documentation service.","intents":["I want to deploy a single Context7 server that my entire team can connect to","I need to run Context7 as a remote service without deploying it locally on each developer's machine","I want to manage authentication and rate limits for a shared documentation server"],"best_for":["Enterprise teams managing documentation access across multiple developers","Organizations with centralized infrastructure who want a shared documentation service","Teams deploying Context7 on cloud platforms (AWS, GCP, Azure, etc.)"],"limitations":["Remote deployment adds network latency (100-500ms) compared to local deployment","Requires infrastructure management (Docker, Kubernetes, or cloud deployment)","Authentication and rate limiting add operational complexity","Single point of failure if remote server goes down; no built-in failover or redundancy","Network bandwidth costs for high-volume documentation queries"],"requires":["Docker or Kubernetes for containerized deployment","Cloud infrastructure (AWS, GCP, Azure) or on-premise servers","Network connectivity between clients and remote server","API key management and authentication infrastructure"],"input_types":["HTTP requests to MCP endpoints","SSE connection requests","authentication tokens (API keys)"],"output_types":["HTTP responses with documentation results","SSE streams for streaming documentation"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-upstash-context7__cap_9","uri":"capability://automation.workflow.local.mcp.server.deployment.with.stdio.transport.for.zero.latency.documentation.access","name":"local mcp server deployment with stdio transport for zero-latency documentation access","description":"Context7 supports local MCP server deployment via stdio transport, enabling developers to run a documentation server on their local machine with direct process communication to their AI editor. 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