{
  "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"]
    }
  ]
}