{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-context-7","slug":"context-7","name":"Context 7","type":"mcp","url":"https://github.com/upstash/context7-mcp","page_url":"https://unfragile.ai/context-7","categories":["mcp-servers","app-builders"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-context-7__cap_0","uri":"capability://search.retrieval.natural.language.library.resolution.with.canonical.id.mapping","name":"natural language library resolution with canonical id mapping","description":"Accepts free-form library names (e.g., 'mongo', 'react hooks') and resolves them to Context7-compatible canonical library IDs through the /v1/search API endpoint. The MCP tool 'resolve-library-id' wraps this API call, encrypting the client IP in the mcp-client-ip header for privacy-preserving analytics. Returns a list of matching library IDs with descriptions, enabling downstream documentation retrieval without requiring users to know exact library identifiers.","intents":["I want to ask about a library by its common name without knowing the exact Context7 ID format","I need to disambiguate between multiple libraries with similar names (e.g., different versions or forks)","I want to discover what libraries are available in the Context7 catalog"],"best_for":["LLM-powered code generation tools (Cursor, Claude, GitHub Copilot) needing to resolve user library queries","IDE plugins that auto-complete library names during prompt composition","Teams building custom MCP clients with fuzzy library search UX"],"limitations":["Requires exact or near-exact library name match — fuzzy matching behavior not documented","Search results limited to libraries pre-indexed in Context7 backend; custom/private libraries not supported","No pagination or result ranking control from client side"],"requires":["Context7 API key (via CONTEXT7_API_KEY env var or --api-key CLI argument)","Network connectivity to Context7 backend API","MCP client supporting tool calling (stdio, HTTP, or SSE transport)"],"input_types":["string (natural language library name)"],"output_types":["structured JSON array of {libraryID: string, description: string}"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-context-7__cap_1","uri":"capability://memory.knowledge.version.specific.documentation.retrieval.with.token.bounded.responses","name":"version-specific documentation retrieval with token-bounded responses","description":"Fetches current, version-specific documentation for a resolved library ID via the GET /v1/:libraryID API endpoint. The 'get-library-docs' MCP tool accepts a canonical library ID, optional topic filter, and token limit (default 5000, minimum 1000), then returns formatted documentation text injected directly into the LLM's context window. Includes Authorization header with API key and X-Context7-Source header for request attribution, enabling the backend to track which MCP clients consume which libraries.","intents":["I want the latest API documentation for a specific library version to generate accurate code","I need to filter documentation to a specific topic (e.g., 'authentication' for an auth library) to reduce context window bloat","I want to control response size to fit within my LLM's context budget"],"best_for":["Code generation agents that need real-time API reference during prompt execution","Cursor IDE users composing prompts with up-to-date library docs","Teams building LLM-powered code assistants that must avoid hallucinated APIs"],"limitations":["Topic filtering behavior not fully documented — unclear if it performs semantic search or keyword matching","Token limit is a hard cap on response size; no pagination or chunking for large documentation sets","Documentation freshness depends on Context7 backend update frequency — not guaranteed real-time for all libraries","No caching at MCP server level — each request hits the backend API, adding ~200-500ms latency per call"],"requires":["Valid Context7 API key with appropriate permissions","Exact canonical library ID (obtained from resolve-library-id tool)","Network connectivity to Context7 backend","MCP client with tool calling support"],"input_types":["string (context7CompatibleLibraryID, e.g., '/mongodb/docs')","string (optional topic filter)","number (optional token limit, 1000-N)"],"output_types":["string (formatted documentation text, plain text or markdown)"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-context-7__cap_2","uri":"capability://tool.use.integration.mcp.server.instantiation.with.multi.transport.support","name":"mcp server instantiation with multi-transport support","description":"Initializes an McpServer instance (src/index.ts) that implements the Model Context Protocol specification, supporting three transport mechanisms: stdio (default, for local IPC), HTTP (for remote clients on configurable port), and SSE (Server-Sent Events, for streaming responses). The server accepts CLI arguments (--transport, --port, --api-key) to configure deployment mode, enabling Context7 to run as a local tool in Cursor, a remote HTTP service, or an SSE-streaming endpoint. Tool registration happens during initialization, binding resolve-library-id and get-library-docs to the MCP request handler.","intents":["I want to run Context7 as a local stdio server integrated with Cursor or VS Code","I need to deploy Context7 as a remote HTTP service accessible to multiple LLM clients","I want to stream documentation responses via SSE for real-time LLM consumption"],"best_for":["Individual developers using Cursor with local MCP server setup","Teams deploying Context7 as a shared microservice behind an API gateway","Organizations building custom MCP clients that require HTTP or SSE transport"],"limitations":["No built-in authentication beyond API key — HTTP transport requires external reverse proxy for TLS/mTLS","Port configuration is static at startup — no dynamic port allocation or load balancing","SSE transport not fully documented in provided DeepWiki excerpt; implementation details unclear","No graceful shutdown or health check endpoints documented"],"requires":["Node.js 18+ (TypeScript runtime)","Context7 API key (CONTEXT7_API_KEY env var or --api-key CLI arg)","For HTTP transport: open port (default 3000) on deployment host","For stdio transport: MCP client that supports stdio IPC (Cursor, Claude Desktop, etc.)"],"input_types":["CLI arguments (--transport, --port, --api-key)"],"output_types":["MCP server instance listening on configured transport"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-context-7__cap_3","uri":"capability://safety.moderation.encrypted.client.ip.tracking.for.privacy.preserving.analytics","name":"encrypted client ip tracking for privacy-preserving analytics","description":"Encrypts the client's IP address in the mcp-client-ip request header before sending it to the Context7 backend API. This header is included in both resolve-library-id and get-library-docs API calls, enabling the backend to track library usage patterns and client distribution without logging raw IP addresses. The encryption approach (algorithm, key management) is not detailed in the provided DeepWiki excerpt, but the pattern ensures privacy compliance while maintaining analytics capability.","intents":["I want to understand library usage patterns across my organization without exposing individual IP addresses","I need to comply with privacy regulations (GDPR, CCPA) while collecting telemetry on Context7 usage","I want to debug client connectivity issues without storing sensitive IP data"],"best_for":["Enterprise teams deploying Context7 with privacy-sensitive requirements","Organizations in regulated industries (finance, healthcare) needing audit trails without raw IP logging","Teams building privacy-first MCP clients"],"limitations":["Encryption algorithm and key management not documented — unclear if encryption is symmetric or asymmetric","No client-side control over IP tracking — always enabled, no opt-out mechanism documented","Encrypted IP format not standardized — may not be interoperable with third-party analytics tools","Decryption capability unclear — unknown if Context7 backend can decrypt IPs or if they're permanently opaque"],"requires":["Context7 MCP server with IP encryption implementation (built-in, no additional config)","Network connectivity to Context7 backend (encryption happens client-side before transmission)"],"input_types":["client IP address (automatic, extracted from request context)"],"output_types":["encrypted IP string in mcp-client-ip header"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-context-7__cap_4","uri":"capability://automation.workflow.library.catalog.indexing.and.configuration.via.context7.json.schema","name":"library catalog indexing and configuration via context7.json schema","description":"Supports a context7.json configuration file that allows library authors and maintainers to define which libraries are indexed in the Context7 catalog, their metadata (name, description, versions), and documentation sources. The schema enables declarative library registration without modifying the Context7 MCP codebase. Libraries are indexed by the Context7 backend during build/deployment, making them discoverable via the resolve-library-id tool. This decouples library management from server deployment, allowing the catalog to grow without server updates.","intents":["I want to add my library to the Context7 catalog so LLMs can access its documentation","I need to specify which versions of my library are documented and how to fetch their docs","I want to update my library's documentation without waiting for Context7 releases"],"best_for":["Open-source library maintainers wanting to make their docs available to LLM-powered code generation","Package registry maintainers (npm, PyPI, Maven) integrating Context7 support","Teams managing internal library catalogs for code generation"],"limitations":["context7.json schema not fully documented in provided excerpt — exact field requirements unclear","No versioning strategy documented for context7.json itself — unclear how schema evolution is handled","Library indexing frequency not specified — unknown how quickly new libraries appear in resolve-library-id results","No validation or linting tools provided for context7.json — authors must manually verify correctness"],"requires":["context7.json file in library repository root (format and location TBD)","Submission process to Context7 backend (not documented in excerpt)","Documentation source accessible to Context7 indexer (URL, API endpoint, or file path)"],"input_types":["JSON file (context7.json) with library metadata"],"output_types":["indexed library entry in Context7 catalog, discoverable via resolve-library-id"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-context-7__cap_5","uri":"capability://tool.use.integration.mcp.tool.registration.and.request.routing","name":"mcp tool registration and request routing","description":"Registers two MCP tools (resolve-library-id and get-library-docs) with the McpServer instance, mapping each tool to a specific API function and parameter schema. The server's request handler routes incoming MCP tool calls to the appropriate function, validates parameters (e.g., enforcing minimum token limit of 1000 for get-library-docs), and returns structured responses. This tool registration pattern follows the MCP specification, enabling LLM clients to discover available tools via the MCP protocol and invoke them with type-safe parameters.","intents":["I want to call library search and documentation retrieval functions from my LLM prompt","I need the MCP client to validate my parameters before sending them to the server","I want to discover what tools are available and their required/optional parameters"],"best_for":["LLM-powered code generation agents that need to invoke library tools as part of reasoning","MCP clients (Cursor, Claude Desktop, custom agents) that support tool calling","Teams building multi-tool MCP servers that need consistent tool registration patterns"],"limitations":["Tool parameters are fixed at registration time — no dynamic parameter generation based on runtime state","Error handling and validation error messages not documented — unclear how parameter validation failures are reported to clients","No tool versioning or deprecation mechanism documented","Tool discovery relies on MCP protocol introspection — no human-readable tool documentation endpoint"],"requires":["MCP client that supports tool calling (Cursor, Claude Desktop, etc.)","Context7 MCP server running with tools registered"],"input_types":["MCP tool call with parameters (libraryName, context7CompatibleLibraryID, topic, tokens)"],"output_types":["structured JSON response from tool function"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-context-7__cap_6","uri":"capability://memory.knowledge.context.window.injection.of.live.documentation.into.llm.prompts","name":"context window injection of live documentation into llm prompts","description":"Retrieves version-specific documentation via get-library-docs and injects the formatted text directly into the LLM's context window, enabling the model to reference current APIs during code generation. The documentation is fetched at prompt time (not training time), ensuring the LLM always has access to the latest library APIs. This pattern addresses the core problem Context7 solves: LLMs trained on historical data generate code using outdated or hallucinated APIs. By injecting live docs into the context, the LLM can generate accurate, version-aware code without retraining.","intents":["I want my LLM to generate code using the latest API version, not outdated training data","I need to prevent API hallucination by providing the LLM with authoritative documentation","I want to ensure generated code doesn't use deprecated APIs or breaking changes"],"best_for":["Code generation agents that must produce production-ready code with current APIs","Teams using LLMs for rapid prototyping where API accuracy is critical","Organizations migrating to new library versions and needing LLM assistance with API changes"],"limitations":["Context window is finite — large documentation sets may exceed available tokens, requiring topic filtering or truncation","LLM may ignore injected documentation if it conflicts with training data or if the prompt doesn't explicitly reference the docs","Documentation quality depends on Context7 backend — if docs are incomplete or inaccurate, LLM will generate incorrect code","No feedback loop to validate that LLM actually used the injected documentation"],"requires":["MCP client that supports tool calling and context injection (Cursor, Claude Desktop, custom agents)","LLM with sufficient context window to accommodate documentation + user prompt + generated code","Context7 MCP server with get-library-docs tool"],"input_types":["user prompt requesting code generation for a specific library"],"output_types":["LLM response with code generated using injected documentation"],"categories":["memory-knowledge","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-context-7__cap_7","uri":"capability://automation.workflow.api.request.attribution.and.source.tracking.via.headers","name":"api request attribution and source tracking via headers","description":"Includes X-Context7-Source header in get-library-docs API calls to track which MCP client (e.g., 'cursor', 'claude-desktop', 'custom-agent') is consuming documentation. This enables the Context7 backend to attribute usage to specific clients and build analytics on which tools are using which libraries. The header is set by the MCP server based on client identification (mechanism not documented in excerpt), allowing the backend to correlate documentation requests with client types without storing raw request metadata.","intents":["I want to understand which MCP clients are using Context7 most heavily","I need to track library usage by client type for capacity planning","I want to identify which clients are generating the most documentation requests"],"best_for":["Context7 operators monitoring service usage and client distribution","Teams building custom MCP clients that want to identify themselves to the backend","Organizations analyzing LLM tool usage patterns across their infrastructure"],"limitations":["Client identification mechanism not documented — unclear how MCP server determines client type","No client-side control over source attribution — always sent, no opt-out mechanism","Source header format not standardized — unclear if it's a free-form string or enumerated value","No client-side access to attribution data — only backend can see which client made which request"],"requires":["Context7 MCP server that sets X-Context7-Source header (built-in, no additional config)","Context7 backend that accepts and logs the header"],"input_types":["client type identifier (automatic, determined by MCP server)"],"output_types":["X-Context7-Source header in API request"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-context-7__cap_8","uri":"capability://tool.use.integration.supported.mcp.client.integration.and.deployment.patterns","name":"supported mcp client integration and deployment patterns","description":"Context7 MCP is designed to integrate with 20+ MCP-compatible clients including Cursor, Claude Desktop, GitHub Copilot (via MCP bridge), and custom agents. Each client has specific installation and configuration requirements (documented in 'Installation and Setup' section of DeepWiki). The MCP server can be deployed as a local stdio process (for Cursor), a remote HTTP service (for cloud-based agents), or an SSE endpoint (for streaming clients). This multi-client support enables Context7 to serve diverse LLM-powered code generation tools from a single server implementation.","intents":["I want to use Context7 with my preferred IDE or LLM client (Cursor, Claude, etc.)","I need to deploy Context7 to multiple clients with different transport requirements","I want to understand which clients are compatible with Context7"],"best_for":["Individual developers using Cursor or Claude Desktop who want to add Context7 support","Teams deploying Context7 to multiple LLM clients (Cursor, Claude, custom agents)","Organizations building custom MCP clients that need Context7 integration"],"limitations":["Client-specific installation instructions not provided in excerpt — must refer to individual client documentation","No unified client management interface — each client requires separate configuration","Compatibility matrix not documented — unclear which client versions support Context7","No automatic client discovery or registration — manual setup required for each client"],"requires":["MCP-compatible client (Cursor, Claude Desktop, etc.)","Context7 MCP server running on compatible transport (stdio, HTTP, or SSE)","Client-specific configuration (e.g., MCP server path for Cursor, HTTP URL for remote clients)"],"input_types":["client configuration (server path, URL, or SSE endpoint)"],"output_types":["integrated MCP tools available in client's LLM context"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":28,"verified":false,"data_access_risk":"high","permissions":["Context7 API key (via CONTEXT7_API_KEY env var or --api-key CLI argument)","Network connectivity to Context7 backend API","MCP client supporting tool calling (stdio, HTTP, or SSE transport)","Valid Context7 API key with appropriate permissions","Exact canonical library ID (obtained from resolve-library-id tool)","Network connectivity to Context7 backend","MCP client with tool calling support","Node.js 18+ (TypeScript runtime)","Context7 API key (CONTEXT7_API_KEY env var or --api-key CLI arg)","For HTTP transport: open port (default 3000) on deployment host"],"failure_modes":["Requires exact or near-exact library name match — fuzzy matching behavior not documented","Search results limited to libraries pre-indexed in Context7 backend; custom/private libraries not supported","No pagination or result ranking control from client side","Topic filtering behavior not fully documented — unclear if it performs semantic search or keyword matching","Token limit is a hard cap on response size; no pagination or chunking for large documentation sets","Documentation freshness depends on Context7 backend update frequency — not guaranteed real-time for all libraries","No caching at MCP server level — each request hits the backend API, adding ~200-500ms latency per call","No built-in authentication beyond API key — HTTP transport requires external reverse proxy for TLS/mTLS","Port configuration is static at startup — no dynamic port allocation or load balancing","SSE transport not fully documented in provided DeepWiki excerpt; implementation details unclear","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.28,"ecosystem":0.49999999999999994,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:03.036Z","last_scraped_at":"2026-05-03T14:00:15.503Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=context-7","compare_url":"https://unfragile.ai/compare?artifact=context-7"}},"signature":"JqyyN5VAyp7PrpTyHHbIFD/pCtmXmA648A6tKJe9zsU32LOg/Zd5z72NmX/dA/7rUm2W9nSWYM0MsqO7z9URBQ==","signedAt":"2026-06-20T08:04:18.902Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/context-7","artifact":"https://unfragile.ai/context-7","verify":"https://unfragile.ai/api/v1/verify?slug=context-7","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"}}