{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-tavily","slug":"tavily","name":"Tavily","type":"mcp","url":"https://github.com/tavily-ai/tavily-mcp","page_url":"https://unfragile.ai/tavily","categories":["mcp-servers","data-pipelines"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-tavily__cap_0","uri":"capability://search.retrieval.real.time.web.search.with.ai.optimized.result.ranking","name":"real-time web search with ai-optimized result ranking","description":"Executes semantic web searches via the Tavily API and returns ranked results optimized for LLM consumption rather than human browsing. The tavily_search tool accepts natural language queries and returns structured result objects containing title, URL, content snippets, and relevance scores. Results are pre-filtered and ranked by Tavily's backend to prioritize informativeness for AI agents, reducing context bloat compared to traditional search APIs.","intents":["I need to find current information about a topic to answer user questions accurately","I want search results formatted for direct LLM consumption without post-processing","I need to verify facts or find sources for claims made in my responses"],"best_for":["AI agents and assistants requiring real-time web knowledge","LLM applications needing fact-grounded responses","Teams building research-oriented AI workflows"],"limitations":["Results depend on Tavily's index freshness — may lag breaking news by minutes","Query complexity affects result quality; overly specific queries may return fewer results","Rate limiting applies based on API tier; high-volume agents may hit quotas"],"requires":["Tavily API key (free tier available)","Network connectivity to Tavily API endpoints","MCP-compatible client (Claude Desktop, Cursor, VS Code, Cline, or OpenAI via mcp-remote)"],"input_types":["text (natural language query string)"],"output_types":["structured JSON with array of result objects containing: title, url, content, score"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-tavily__cap_1","uri":"capability://data.processing.analysis.autonomous.web.content.extraction.with.structured.output","name":"autonomous web content extraction with structured output","description":"Extracts and structures full-page content from URLs using the tavily_extract tool, which crawls target pages and returns cleaned, markdown-formatted text with metadata. The tool handles JavaScript-rendered content, removes boilerplate (navigation, ads, footers), and preserves semantic structure. Extraction is performed server-side by Tavily, eliminating the need for client-side browser automation or DOM parsing.","intents":["I need to get the full text content of a webpage without loading it in a browser","I want to extract article content while filtering out ads and navigation clutter","I need to process multiple URLs and get structured markdown output for analysis"],"best_for":["Content analysis and summarization workflows","Research agents that need to read full articles","Document processing pipelines that consume web content"],"limitations":["Extraction quality varies by page structure; complex single-page apps may not extract cleanly","JavaScript-heavy sites with dynamic content loading may miss some content","Large pages (>10MB) may be truncated or timeout","No support for authenticated/paywalled content"],"requires":["Tavily API key","Valid, publicly accessible URL","Network connectivity to target URL and Tavily API"],"input_types":["text (URL string)"],"output_types":["structured JSON containing: raw_content (markdown), cleaned_content, metadata (title, author, publish_date)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-tavily__cap_10","uri":"capability://code.generation.editing.typescript.implementation.with.axios.http.client","name":"typescript implementation with axios http client","description":"Tavily MCP is implemented in TypeScript and compiled to a Node.js executable, using axios for HTTP communication with Tavily's REST API. The codebase uses the MCP SDK (from @modelcontextprotocol/sdk) for protocol implementation and StdioServerTransport for local deployment. Type safety is enforced through TypeScript interfaces for tool parameters and API responses, reducing runtime errors.","intents":["I want to extend or modify Tavily MCP's behavior by editing the source code","I need to understand the implementation details for debugging or customization","I want to build a similar MCP server using Tavily MCP as a reference"],"best_for":["Developers comfortable with TypeScript and Node.js","Teams needing to customize or extend Tavily MCP","Organizations building custom MCP servers"],"limitations":["TypeScript-only; no Python or other language implementations","Requires Node.js 18+ for runtime","Build process requires npm and TypeScript compiler","No official type definitions for Tavily API; types are inferred from responses"],"requires":["Node.js 18+","npm or yarn","TypeScript knowledge (for modifications)","Understanding of MCP protocol (for extensions)"],"input_types":["TypeScript source code"],"output_types":["Compiled Node.js executable"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-tavily__cap_11","uri":"capability://automation.workflow.docker.containerization.for.isolated.deployment","name":"docker containerization for isolated deployment","description":"Tavily MCP provides a Dockerfile for containerized deployment, enabling isolated execution in Docker environments. The container includes Node.js runtime, dependencies, and the compiled MCP server, with environment variable injection for API key configuration. Docker deployment is suitable for Kubernetes, serverless platforms, and air-gapped environments where local installation is impractical.","intents":["I want to deploy Tavily MCP in a Docker container for isolation and reproducibility","I need to run Tavily MCP in Kubernetes or a container orchestration platform","I want to ensure consistent deployment across development and production environments"],"best_for":["Teams using Docker/Kubernetes for infrastructure","Organizations requiring containerized deployments","Developers needing reproducible, isolated environments"],"limitations":["Requires Docker runtime; adds container overhead (~50-100ms startup time)","Image size includes Node.js runtime; not suitable for minimal/serverless environments","Networking must be configured to expose MCP server to clients","Debugging inside containers requires additional tooling"],"requires":["Docker runtime (Docker Desktop, Docker Engine, or compatible)","Dockerfile (provided in repository)","Tavily API key for environment variable injection"],"input_types":["Dockerfile, environment variables"],"output_types":["Docker image, running container with MCP server"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-tavily__cap_2","uri":"capability://planning.reasoning.autonomous.multi.step.web.research.with.iterative.refinement","name":"autonomous multi-step web research with iterative refinement","description":"The tavily_research tool orchestrates multi-step research workflows where the agent autonomously searches, extracts, and synthesizes information across multiple sources. Unlike single-query search, this tool accepts a research goal and iteratively refines queries based on findings, performing up to N searches and extractions in a single call. Tavily's backend manages the research loop, returning a comprehensive research report with citations.","intents":["I need to conduct deep research on a topic without manually chaining multiple search/extract calls","I want the agent to autonomously refine its search strategy based on intermediate findings","I need a research report with multiple sources synthesized into a coherent narrative"],"best_for":["Research-heavy AI agents and assistants","Competitive intelligence and market research workflows","Investigative journalism and fact-checking applications","Teams wanting to reduce prompt engineering for multi-step research"],"limitations":["Research depth and iteration count depend on API tier; free tier may limit iterations","Execution time is longer than single search (typically 10-30 seconds for full research)","Agent has no direct control over search refinement strategy; Tavily's heuristics determine next queries","May over-search on ambiguous topics, consuming quota quickly"],"requires":["Tavily API key (higher tier recommended for multi-iteration research)","Research goal or topic description (text)","Network connectivity and patience for async research execution"],"input_types":["text (research goal/topic description)"],"output_types":["structured JSON containing: research_report (markdown), sources (array of URLs with summaries), key_findings (array)"],"categories":["planning-reasoning","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-tavily__cap_3","uri":"capability://data.processing.analysis.web.content.crawling.with.recursive.link.discovery","name":"web content crawling with recursive link discovery","description":"The tavily_crawl tool recursively crawls websites starting from a seed URL, discovering and extracting content from linked pages up to a configurable depth. The tool returns a structured map of crawled pages with extracted content, metadata, and link relationships. Crawling is performed server-side with automatic deduplication and cycle detection, returning results as a graph structure suitable for knowledge base construction.","intents":["I need to extract content from an entire website or documentation site programmatically","I want to build a knowledge base from a website's content hierarchy","I need to discover all pages under a domain and extract their content in one operation"],"best_for":["Documentation site indexing and knowledge base construction","Competitive website analysis and content mapping","Building RAG systems from website content","Teams needing to ingest entire sites without manual URL enumeration"],"limitations":["Crawl depth and page limits depend on API tier; free tier may limit to shallow crawls","Crawling large sites (>1000 pages) may timeout or hit quota limits","Respects robots.txt; cannot crawl disallowed paths","No support for authenticated/paywalled content behind login","Crawl time scales with site size; large sites may take 30+ seconds"],"requires":["Tavily API key","Valid seed URL (domain root or specific page)","Site must allow crawling (robots.txt must permit)","Network connectivity"],"input_types":["text (seed URL)","optional: depth parameter (integer, typically 1-3)","optional: max_pages parameter (integer)"],"output_types":["structured JSON containing: pages (array of {url, content, title, links}), graph (adjacency structure), metadata"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-tavily__cap_4","uri":"capability://data.processing.analysis.website.structure.mapping.and.link.graph.analysis","name":"website structure mapping and link graph analysis","description":"The tavily_map tool generates a structural map of a website, returning the link graph, page hierarchy, and metadata without extracting full content. This lightweight operation discovers all pages, their relationships, and basic metadata (title, description) in a single call. The tool is useful for understanding site structure before deciding which pages to crawl or extract in detail.","intents":["I need to understand a website's structure and page hierarchy before crawling it","I want to identify all pages on a site without downloading their full content","I need to analyze link patterns and site organization for SEO or content mapping"],"best_for":["Site reconnaissance and structure analysis","Planning targeted crawls of specific site sections","Content auditing and site organization assessment","Teams needing lightweight site mapping before heavy extraction"],"limitations":["Returns structure only; does not extract page content","Large sites (>5000 pages) may return truncated link graphs","Does not follow external links; maps only internal site structure","Execution time scales with site size but is faster than full crawl"],"requires":["Tavily API key","Valid seed URL (domain root or specific page)","Network connectivity"],"input_types":["text (seed URL)"],"output_types":["structured JSON containing: pages (array of {url, title, description}), links (adjacency list), hierarchy (tree structure)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-tavily__cap_5","uri":"capability://tool.use.integration.mcp.tool.registration.and.schema.based.function.calling","name":"mcp tool registration and schema-based function calling","description":"Tavily MCP implements the Model Context Protocol (MCP) specification, registering the five tools (search, extract, crawl, map, research) as callable functions with JSON Schema definitions. The server uses MCP's ListToolsRequestSchema and CallToolRequestSchema to expose tools to compatible clients. Tool schemas define parameters, types, and descriptions, enabling clients to understand and invoke tools without hardcoded knowledge of Tavily's API.","intents":["I want to integrate Tavily's web tools into my MCP-compatible client without custom API bindings","I need standardized tool schemas that work across different LLM clients (Claude, Cursor, VS Code)","I want to enable my AI agent to call web tools through a standard protocol"],"best_for":["Developers building MCP-compatible AI clients","Teams standardizing on MCP for tool integration","LLM applications requiring interoperability across multiple client platforms"],"limitations":["Requires MCP-compatible client; non-MCP clients cannot use this server","Tool schemas are static; cannot be dynamically modified at runtime","MCP transport overhead adds ~50-100ms per tool call vs direct API","Debugging MCP communication requires understanding MCP protocol details"],"requires":["MCP-compatible client (Claude Desktop, Cursor, VS Code, Cline, or mcp-remote bridge)","Tavily API key configured in client or environment","Node.js 18+ (for local deployment)"],"input_types":["MCP CallToolRequest with tool name and parameters object"],"output_types":["MCP CallToolResult with content array (text or error)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-tavily__cap_6","uri":"capability://tool.use.integration.multi.client.deployment.with.unified.tool.interface","name":"multi-client deployment with unified tool interface","description":"Tavily MCP supports deployment as both a remote hosted service (https://mcp.tavily.com/mcp/) and local self-hosted instances, with identical tool interfaces across all deployment modes. The server uses StdioServerTransport for local deployment and HTTP/OAuth for remote deployment, abstracting transport differences from clients. A single tool registration handles both deployment patterns, enabling agents to use Tavily tools regardless of deployment choice.","intents":["I want to use Tavily tools in Claude Desktop without managing a local server","I need to self-host Tavily MCP for privacy or air-gapped environments","I want to switch between remote and local deployment without changing client configuration"],"best_for":["Teams evaluating remote vs self-hosted deployment","Organizations with data residency or privacy requirements","Developers wanting zero-setup integration (remote) or full control (local)"],"limitations":["Remote deployment requires internet connectivity; no offline mode","Local deployment requires Node.js 18+ and manual setup (NPX, Docker, or Git)","Remote deployment adds network latency (~100-500ms) vs local (~10-50ms)","Local Docker deployment requires Docker runtime; adds container overhead"],"requires":["For remote: Tavily API key, internet connectivity","For local NPX: Node.js 18+, npm, Tavily API key","For local Docker: Docker runtime, Tavily API key","For local Git: Node.js 18+, npm, git, build tools"],"input_types":["MCP protocol messages (identical across deployments)"],"output_types":["MCP protocol responses (identical across deployments)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-tavily__cap_7","uri":"capability://tool.use.integration.client.integration.with.automatic.capability.discovery","name":"client integration with automatic capability discovery","description":"Tavily MCP integrates with multiple LLM clients (Claude Desktop, Cursor, VS Code, Cline, OpenAI via mcp-remote) through MCP's capability discovery mechanism. Clients query the server's ListToolsRequest to discover available tools and their schemas at startup, enabling dynamic tool loading without hardcoded tool definitions. The server advertises tool names, descriptions, and parameter schemas, allowing clients to present tools to users and validate inputs.","intents":["I want my LLM client to automatically discover and load Tavily tools without manual configuration","I need tool descriptions and schemas to be visible in my client's UI","I want to enable/disable Tavily tools dynamically without restarting my client"],"best_for":["LLM client developers integrating MCP servers","End users wanting plug-and-play tool integration","Teams managing multiple MCP servers with dynamic tool discovery"],"limitations":["Capability discovery is one-way; clients cannot modify tool schemas at runtime","Tool availability depends on API key validity; expired keys may cause silent failures","Discovery latency adds ~50-100ms to client startup","No support for conditional tool availability based on user permissions"],"requires":["MCP-compatible client with capability discovery support","Valid Tavily API key accessible to the server","Network connectivity between client and server"],"input_types":["MCP ListToolsRequest"],"output_types":["MCP ListToolsResult with array of tool definitions (name, description, inputSchema)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-tavily__cap_8","uri":"capability://safety.moderation.api.key.management.with.environment.and.url.parameter.support","name":"api key management with environment and url parameter support","description":"Tavily MCP supports multiple authentication patterns depending on deployment mode: local deployments use TAVILY_API_KEY environment variable, remote deployments support URL query parameters (?tavilyApiKey=<key>) and Authorization headers (Bearer <key>), and OAuth flow for token-based authentication. The server validates API keys before executing tool calls, returning authentication errors if keys are invalid or expired.","intents":["I want to securely provide my Tavily API key to the MCP server without hardcoding it","I need to use different API keys for different clients or environments","I want to rotate API keys without restarting the server"],"best_for":["Teams managing API keys across multiple environments (dev, staging, prod)","Organizations requiring secure credential management","Developers deploying Tavily MCP in containerized or serverless environments"],"limitations":["Environment variable approach requires server restart to rotate keys","URL parameter approach exposes keys in logs/URLs; not recommended for production","OAuth flow requires additional setup and token refresh logic","No built-in key rotation or expiration management; relies on external systems"],"requires":["Valid Tavily API key (obtain from https://tavily.com/)","For local: ability to set environment variables","For remote: ability to pass URL parameters or headers","For OAuth: Tavily OAuth provider configuration"],"input_types":["text (API key string or OAuth token)"],"output_types":["authentication success/failure response"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-tavily__cap_9","uri":"capability://tool.use.integration.request.processing.with.error.handling.and.response.formatting","name":"request processing with error handling and response formatting","description":"Tavily MCP implements request processing that validates tool parameters against JSON schemas, executes API calls via axios, and formats responses as MCP CallToolResult objects. The server catches API errors (rate limits, invalid URLs, network failures) and returns structured error messages to clients. Response formatting converts Tavily API responses into MCP-compatible structures with content arrays, enabling consistent error handling across all tools.","intents":["I want clear error messages when tool calls fail (invalid URL, rate limit, API error)","I need tool responses formatted consistently across all five tools","I want the server to validate parameters before making expensive API calls"],"best_for":["Developers building robust MCP clients that handle errors gracefully","Teams needing detailed error diagnostics for debugging","Applications requiring consistent response formatting across tools"],"limitations":["Parameter validation is schema-based; complex validation logic must be in Tavily API","Error messages depend on Tavily API error responses; may be generic for some failures","No retry logic; clients must implement their own retry strategies","Response formatting adds ~10-20ms overhead per request"],"requires":["Valid MCP CallToolRequest with tool name and parameters","Tavily API key for authentication","Network connectivity to Tavily API"],"input_types":["MCP CallToolRequest (tool name, parameters object)"],"output_types":["MCP CallToolResult with content array (text response or error message)"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":29,"verified":false,"data_access_risk":"high","permissions":["Tavily API key (free tier available)","Network connectivity to Tavily API endpoints","MCP-compatible client (Claude Desktop, Cursor, VS Code, Cline, or OpenAI via mcp-remote)","Tavily API key","Valid, publicly accessible URL","Network connectivity to target URL and Tavily API","Node.js 18+","npm or yarn","TypeScript knowledge (for modifications)","Understanding of MCP protocol (for extensions)"],"failure_modes":["Results depend on Tavily's index freshness — may lag breaking news by minutes","Query complexity affects result quality; overly specific queries may return fewer results","Rate limiting applies based on API tier; high-volume agents may hit quotas","Extraction quality varies by page structure; complex single-page apps may not extract cleanly","JavaScript-heavy sites with dynamic content loading may miss some content","Large pages (>10MB) may be truncated or timeout","No support for authenticated/paywalled content","TypeScript-only; no Python or other language implementations","Requires Node.js 18+ for runtime","Build process requires npm and TypeScript compiler","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.34,"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":"2026-06-17T09:51:04.050Z","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=tavily","compare_url":"https://unfragile.ai/compare?artifact=tavily"}},"signature":"00U3flM1x/5shWn7hPL8nU4On9fTt3ESQC6GYtGcP58gqC54PcIQ+yNPePb+54j72c6eMu9qr+QI+cVnmarrDA==","signedAt":"2026-06-22T10:48:29.883Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/tavily","artifact":"https://unfragile.ai/tavily","verify":"https://unfragile.ai/api/v1/verify?slug=tavily","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"}}