{ "name": "Tavily", "description": "Real-time search engine for AI agents and RAG workflows. Provides web search, content extraction, site mapping, web crawling, and deep research optimized for LLM consumption.", "url": "https://api.tavily.com", "version": "1.0.0", "protocolVersion": "1.0", "provider": { "organization": "Tavily", "url": "https://tavily.com" }, "documentationUrl": "https://docs.tavily.com", "iconUrl": "https://tavily.com/favicon.ico", "capabilities": { "streaming": true, "pushNotifications": false, "stateTransitionHistory": false }, "securitySchemes": { "apiKey": { "type": "apiKey", "name": "Authorization", "in": "header", "description": "Tavily API key passed as Bearer token for REST API" }, "oauth2": { "type": "oauth2", "flows": { "authorizationCode": { "authorizationUrl": "https://mcp.tavily.com/mcp/", "tokenUrl": "https://mcp.tavily.com/mcp/", "scopes": {} } }, "description": "OAuth 2.0 authentication for MCP server connections" } }, "security": [ { "apiKey": [] }, { "oauth2": [] } ], "defaultInputModes": [ "application/json" ], "defaultOutputModes": [ "application/json" ], "skills": [ { "id": "web-search", "name": "Web Search", "description": "AI-optimized web search returning relevant results with content snippets and optional answer generation. Supports topic filtering (general, news, finance), search depth control, time-range filtering, and domain inclusion/exclusion.", "tags": [ "search", "web", "research", "news", "information-retrieval", "rag" ], "examples": [ "Search for the latest developments in quantum computing", "Find news articles about AI regulation from the past week", "Research best practices for Kubernetes autoscaling" ], "inputModes": [ "application/json", "text/plain" ], "outputModes": [ "application/json" ] }, { "id": "content-extract", "name": "Content Extract", "description": "Extracts clean, structured content from one or more URLs. Strips navigation, ads, and boilerplate, returning text optimized for LLM consumption.", "tags": [ "extract", "scraping", "content", "parsing", "url" ], "examples": [ "Extract the main article content from this URL", "Get the text content from these documentation pages" ], "inputModes": [ "application/json" ], "outputModes": [ "application/json" ] }, { "id": "web-crawl", "name": "Web Crawl", "description": "Crawls a website with configurable depth control, extracting content from discovered pages. Returns structured content from multiple pages within a domain.", "tags": [ "crawl", "spider", "website", "depth-search" ], "examples": [ "Crawl the documentation site at docs.example.com up to depth 3", "Crawl example.com and extract all product pages" ], "inputModes": [ "application/json" ], "outputModes": [ "application/json" ] }, { "id": "site-map", "name": "Site Map", "description": "Maps the structure of a website, discovering all accessible URLs and their relationships. Returns a comprehensive URL list for a given domain.", "tags": [ "map", "sitemap", "url-discovery", "website-structure" ], "examples": [ "Map all URLs on docs.example.com", "Discover the site structure of example.com" ], "inputModes": [ "application/json" ], "outputModes": [ "application/json" ] }, { "id": "deep-research", "name": "Deep Research", "description": "Async deep research that searches, extracts, and synthesizes information into comprehensive reports. Supports streaming and polling for long-running research tasks.", "tags": [ "research", "report", "analysis", "synthesis", "deep-search" ], "examples": [ "Research the competitive landscape for AI search APIs", "Generate a comprehensive report on recent advances in RAG architectures", "Analyze the pros and cons of different vector databases" ], "inputModes": [ "application/json" ], "outputModes": [ "application/json", "text/event-stream" ] } ] }