Card snapshot
www.tavily.com
·
2026-11-02 18:07:03 UTC
·
2ed3c035800e7e8215122598de5ff4604fececc18fe5005dbc9c72bbbc0b20b9
This is a frozen copy of the agent's agent-card.json as we observed it at the timestamp above. We capture a new snapshot every time the card's content hash changes. Useful for: forensic drift analysis, verifying downstream callers see the right version, reproducing routing decisions made historically.
{
"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/app-icons/icon-512.png",
"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"
]
}
]
}