{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tavily-mcp-server","slug":"tavily-mcp-server","name":"Tavily MCP Server","type":"mcp","url":"https://github.com/tavily-ai/tavily-mcp","page_url":"https://unfragile.ai/tavily-mcp-server","categories":["mcp-servers","data-pipelines"],"tags":["tavily","search","research","official"],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tavily-mcp-server__cap_0","uri":"capability://search.retrieval.real.time.web.search.with.llm.optimized.result.formatting","name":"real-time web search with llm-optimized result formatting","description":"Executes web searches via the Tavily API and returns structured results with relevance scoring, source attribution, and clean text extraction optimized for LLM consumption. The MCP server marshals search queries through an axios HTTP client configured with the Tavily API key, parses JSON responses containing ranked results with URLs and snippets, and formats output for direct consumption by language models without additional preprocessing.","intents":["I need my AI agent to search the web for current information and get back clean, ranked results with sources","I want to augment my LLM with real-time web data without building my own search infrastructure","I need search results formatted specifically for LLM context windows, not for human reading"],"best_for":["AI agents and assistants requiring real-time information retrieval","Teams building research-augmented LLM applications","Developers integrating web search into Claude Desktop, Cursor, or VS Code workflows"],"limitations":["Requires valid Tavily API key with active quota; rate limits depend on API plan tier","Search results are time-dependent and may vary based on Tavily's web crawl freshness","No built-in caching of results — each query incurs an API call"],"requires":["Tavily API key (free tier available)","Node.js 18+ for local deployment or access to remote MCP server at https://mcp.tavily.com/mcp/","MCP-compatible client (Claude Desktop, Cursor, VS Code with MCP extension, or Cline)"],"input_types":["text query string","optional parameters: max_results (integer), search_depth (basic/advanced), include_domains (array), exclude_domains (array)"],"output_types":["structured JSON with array of results","each result contains: title, url, content snippet, relevance score, source domain"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tavily-mcp-server__cap_1","uri":"capability://data.processing.analysis.autonomous.web.content.extraction.with.structured.output","name":"autonomous web content extraction with structured output","description":"Extracts clean, structured content from specified URLs using the Tavily extract endpoint, handling HTML parsing, boilerplate removal, and content normalization automatically. The server sends URLs to Tavily's extraction service via axios, receives parsed markdown or structured text, and returns content ready for LLM ingestion without requiring the client to manage web scraping libraries or HTML parsing.","intents":["I need to extract the main content from a specific URL without dealing with HTML parsing or boilerplate","I want my AI agent to read and understand web pages it discovers during research","I need clean, structured text from web content for RAG or context injection into LLMs"],"best_for":["Research agents that need to read full page content from search results","RAG pipelines requiring clean web content extraction","Developers building multi-step research workflows where agents must analyze page content"],"limitations":["Extraction quality depends on page structure and Tavily's parsing heuristics; complex layouts may lose formatting","No support for JavaScript-rendered content — only static HTML is processed","Large pages may be truncated to fit API response limits"],"requires":["Tavily API key with extract capability enabled","Valid, publicly accessible URL","MCP-compatible client with tool-calling support"],"input_types":["text URL string","optional: extraction_mode (markdown/raw)"],"output_types":["structured text (markdown or raw HTML-stripped content)","metadata: extracted_at timestamp, source_url, content_length"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tavily-mcp-server__cap_10","uri":"capability://tool.use.integration.client.specific.integration.templates.for.claude.desktop.cursor.vs.code.and.cline","name":"client-specific integration templates for claude desktop, cursor, vs code, and cline","description":"Provides pre-built configuration templates and integration guides for popular MCP clients (Claude Desktop, Cursor, VS Code, Cline), including JSON configuration snippets for claude_desktop_config.json, cursor settings, VS Code extensions, and Cline agent configuration. Each integration template specifies the MCP server command, environment variables, and client-specific setup steps.","intents":["I want to add Tavily MCP to my Claude Desktop setup without figuring out the config format","I need to integrate Tavily into my Cursor workflow quickly","I want to use Tavily tools in my VS Code MCP extension"],"best_for":["Users of Claude Desktop, Cursor, VS Code, and Cline wanting quick Tavily integration","Teams standardizing MCP server configurations across their organization","Developers building custom MCP clients who need reference implementations"],"limitations":["Templates are client-specific; configuration format varies between clients","Templates assume standard installation paths; custom installations may require manual path adjustment","No automated configuration validation; users must verify config syntax manually","Templates may lag behind client updates; users may need to adjust for new client versions"],"requires":["Target MCP client installed (Claude Desktop, Cursor, VS Code, or Cline)","Tavily MCP server installed locally (via NPX, Docker, or Git)","Tavily API key"],"input_types":["client-specific configuration file (JSON or YAML)"],"output_types":["configured MCP client with Tavily tools available"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tavily-mcp-server__cap_2","uri":"capability://data.processing.analysis.recursive.web.crawling.with.depth.control","name":"recursive web crawling with depth control","description":"Crawls websites starting from a seed URL and recursively follows internal links up to a specified depth, extracting content from each page and returning a structured collection of crawled pages. The server manages crawl state through Tavily's crawl endpoint, controlling recursion depth and link-following behavior, and returns all discovered pages with their extracted content and metadata for bulk analysis or knowledge base construction.","intents":["I need to crawl an entire website or section to build a knowledge base for my AI agent","I want to discover and extract content from multiple related pages without manual URL enumeration","I need to analyze a site's structure and content at scale for research or competitive analysis"],"best_for":["Building domain-specific knowledge bases from websites","Research agents that need comprehensive coverage of a topic across multiple pages","Teams creating RAG datasets from website content"],"limitations":["Crawl depth is limited to prevent runaway crawls; deep crawls may timeout or hit API limits","Only follows internal links; cannot crawl across domains","Respects robots.txt and site crawl policies; some sites may block crawling","No JavaScript execution — only static HTML content is crawled"],"requires":["Tavily API key with crawl capability","Valid seed URL","Patience for crawl completion; large sites may take minutes"],"input_types":["text seed URL","optional: max_depth (integer, typically 1-3), include_patterns (array of URL patterns)"],"output_types":["array of crawled pages","each page contains: url, extracted_content, title, metadata, crawl_depth"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tavily-mcp-server__cap_3","uri":"capability://search.retrieval.semantic.url.mapping.and.site.structure.discovery","name":"semantic url mapping and site structure discovery","description":"Analyzes a website's structure and generates a semantic map of URLs organized by topic or content type, enabling agents to understand site organization without manual exploration. The tavily_map tool sends a seed URL to Tavily's mapping service, which crawls the site, clusters pages by semantic similarity, and returns a hierarchical structure of discovered URLs grouped by inferred topic or purpose.","intents":["I want my agent to understand a website's structure and find relevant pages without crawling everything","I need to discover topic-specific sections of a large website for targeted research","I want to map a competitor's website structure to understand their content organization"],"best_for":["Research agents navigating large or unfamiliar websites","Competitive analysis workflows requiring site structure understanding","Knowledge base builders needing to identify high-value content sections"],"limitations":["Semantic clustering quality depends on page content and Tavily's NLP models; may misclassify pages","Only maps publicly crawlable content; respects robots.txt restrictions","Large sites may return truncated maps due to API response limits"],"requires":["Tavily API key with map capability","Valid seed URL","MCP-compatible client"],"input_types":["text seed URL","optional: max_pages (integer), include_patterns (array)"],"output_types":["hierarchical structure of URLs","each node contains: url, inferred_topic, content_type, child_urls, relevance_score"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tavily-mcp-server__cap_4","uri":"capability://planning.reasoning.autonomous.multi.step.research.with.agent.orchestration","name":"autonomous multi-step research with agent orchestration","description":"Orchestrates multi-step research workflows where an agent autonomously decides which search, extraction, and crawling steps to perform based on intermediate results. The tavily_research tool wraps the other four tools and manages state across multiple API calls, allowing agents to refine queries, follow promising leads, and synthesize findings without explicit step-by-step instruction from the user.","intents":["I want my agent to conduct research autonomously, deciding what to search and what to read based on findings","I need a research agent that can follow research threads and synthesize information from multiple sources","I want to delegate complex research tasks to an AI agent without specifying exact steps"],"best_for":["Autonomous research agents and assistants","Complex research workflows requiring multi-step reasoning and source synthesis","Teams building AI-powered research tools or competitive intelligence systems"],"limitations":["Research quality depends on agent reasoning and Tavily's underlying search/extraction quality","No guaranteed convergence — agents may loop or exhaust API quota on poorly-scoped research tasks","Requires careful prompt engineering to guide agent behavior and prevent runaway queries"],"requires":["Tavily API key with research capability","MCP-compatible client with strong reasoning capabilities (Claude 3.5+ recommended)","Clear research objective or query to guide agent exploration"],"input_types":["text research query or objective","optional: max_steps (integer), focus_areas (array), exclude_domains (array)"],"output_types":["structured research report","contains: findings (array of key facts), sources (array of URLs), synthesis (summary), confidence_scores"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tavily-mcp-server__cap_5","uri":"capability://tool.use.integration.mcp.protocol.bridging.with.multiple.client.integrations","name":"mcp protocol bridging with multiple client integrations","description":"Implements the Model Context Protocol (MCP) server specification using TypeScript and StdioServerTransport, enabling the Tavily tools to be exposed as MCP tools callable by any MCP-compatible client. The server registers tool handlers via setRequestHandler(ListToolsRequestSchema, ...) and CallToolRequestSchema, marshaling tool calls from clients through to Tavily API endpoints and returning results in MCP-compliant format.","intents":["I want to use Tavily search in Claude Desktop without custom integration code","I need Tavily tools available in my Cursor or VS Code workflow via MCP","I want to integrate Tavily into an MCP-compatible client I'm building"],"best_for":["Developers using Claude Desktop, Cursor, VS Code, or other MCP-compatible clients","Teams building custom MCP clients and needing pre-built Tavily integration","Users wanting zero-configuration Tavily access in their AI workflows"],"limitations":["Requires MCP-compatible client; not compatible with non-MCP tools or APIs","StdioServerTransport adds ~50-100ms latency per tool call due to process communication overhead","Tool discovery and schema validation happens at client initialization; schema changes require client restart"],"requires":["MCP-compatible client (Claude Desktop, Cursor, VS Code with MCP extension, Cline, or custom MCP client)","Node.js 18+ for local deployment, or access to remote server at https://mcp.tavily.com/mcp/","Tavily API key"],"input_types":["MCP CallToolRequest with tool name and arguments","tool arguments vary by tool (search, extract, crawl, map, research)"],"output_types":["MCP CallToolResult with tool output","content array containing text/structured results and optional error messages"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tavily-mcp-server__cap_6","uri":"capability://automation.workflow.dual.deployment.architecture.remote.and.local","name":"dual deployment architecture (remote and local)","description":"Supports both remote deployment (hosted at https://mcp.tavily.com/mcp/) and local self-hosted deployment (via NPX, Docker, or Git), with different authentication models for each. Remote deployment uses URL parameters or Bearer token headers for API key passing, while local deployment uses TAVILY_API_KEY environment variable. Both expose identical tool capabilities through the same MCP interface.","intents":["I want to use Tavily MCP immediately without installing anything locally","I need to self-host Tavily MCP for security or compliance reasons","I want to deploy Tavily MCP in a Docker container for my infrastructure"],"best_for":["Users wanting zero-setup Tavily MCP access via remote server","Teams with security requirements for local/self-hosted deployment","DevOps teams containerizing MCP servers for orchestrated environments"],"limitations":["Remote deployment adds network latency (~100-500ms per request) vs local deployment","Remote deployment requires passing API key via URL or header; local deployment uses environment variables (more secure)","Local Docker deployment requires Docker runtime and image build/pull time","NPX deployment requires Node.js 18+ and npm/yarn on the system"],"requires":["For remote: Tavily API key, internet connectivity, MCP-compatible client configured with remote server URL","For local NPX: Node.js 18+, npm/yarn, Tavily API key in TAVILY_API_KEY environment variable","For local Docker: Docker runtime, Dockerfile (provided), Tavily API key passed as environment variable"],"input_types":["deployment configuration (remote URL or local environment setup)"],"output_types":["running MCP server exposing five Tavily tools"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tavily-mcp-server__cap_7","uri":"capability://safety.moderation.api.key.management.with.environment.variable.and.header.based.authentication","name":"api key management with environment variable and header-based authentication","description":"Manages Tavily API authentication through multiple methods depending on deployment context: local deployment reads TAVILY_API_KEY from environment variables, remote deployment accepts API keys via URL query parameters (?tavilyApiKey=<key>) or Authorization Bearer headers. The server validates API key presence on initialization and includes it in all axios requests to Tavily endpoints.","intents":["I need to securely pass my Tavily API key to the MCP server without hardcoding it","I want to use Tavily MCP in a CI/CD pipeline with environment-based secrets","I need to rotate or manage multiple API keys for different deployments"],"best_for":["DevOps teams managing secrets in CI/CD pipelines","Local deployment scenarios where environment variables are preferred","Remote deployment users who want to pass API keys securely via headers"],"limitations":["URL parameter method (?tavilyApiKey=<key>) exposes API key in URLs; not recommended for production","Environment variable method requires shell access to set variables; not suitable for browser-based clients","Bearer token method requires client to include Authorization header; not all MCP clients support custom headers","No built-in key rotation or expiration; key management is delegated to user infrastructure"],"requires":["Valid Tavily API key from https://tavily.com","For local: ability to set environment variables in the deployment shell","For remote: ability to pass URL parameters or HTTP headers from MCP client"],"input_types":["API key string (from environment variable, URL parameter, or HTTP header)"],"output_types":["authenticated axios instance configured with API key for all Tavily API calls"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tavily-mcp-server__cap_8","uri":"capability://tool.use.integration.tool.schema.registration.and.discovery.via.mcp.listtoolsrequest","name":"tool schema registration and discovery via mcp listtoolsrequest","description":"Registers five Tavily tools with the MCP server using setRequestHandler(ListToolsRequestSchema, ...), defining tool names, descriptions, and JSON schemas for input parameters. When MCP clients request available tools via ListToolsRequest, the server responds with complete tool metadata including parameter schemas, enabling clients to validate tool calls and provide UI hints for tool usage.","intents":["I want my MCP client to discover available Tavily tools and their parameters automatically","I need to validate tool arguments before calling Tavily to catch errors early","I want to provide users with UI hints about available tools and their parameters"],"best_for":["MCP client developers building tool discovery and validation","Users of Claude Desktop and other MCP clients that display available tools","Teams building custom MCP clients with tool parameter validation"],"limitations":["Schema discovery happens at client initialization; schema changes require client restart","JSON schema validation is client-side responsibility; server does not validate argument types","No runtime parameter validation on the server; invalid arguments are passed through to Tavily API"],"requires":["MCP-compatible client that supports ListToolsRequest","Running Tavily MCP server (remote or local)"],"input_types":["MCP ListToolsRequest (no parameters)"],"output_types":["array of Tool objects containing: name, description, inputSchema (JSON schema)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tavily-mcp-server__cap_9","uri":"capability://safety.moderation.error.handling.and.api.response.normalization","name":"error handling and api response normalization","description":"Wraps Tavily API calls in try-catch blocks and normalizes error responses into MCP-compliant format, converting HTTP errors, API validation errors, and network failures into structured error messages returned to clients. The server catches axios errors, extracts error details from Tavily API responses, and returns them as MCP CallToolResult with error content type.","intents":["I want clear error messages when Tavily API calls fail","I need to handle API quota exhaustion or rate limiting gracefully in my agent","I want to distinguish between client errors (bad parameters) and server errors (API failures)"],"best_for":["Developers building robust agents that handle API failures","Teams monitoring MCP server health and error rates","Users debugging tool call failures"],"limitations":["Error messages are limited to what Tavily API returns; some errors may lack detail","No built-in retry logic; clients must implement retries if needed","Rate limit errors are not distinguished from other API errors; clients must parse error messages","Network timeouts are not configurable; default axios timeout applies"],"requires":["Running Tavily MCP server","MCP-compatible client that can handle error responses"],"input_types":["any tool call that may fail (search, extract, crawl, map, research)"],"output_types":["MCP CallToolResult with error content type","error message string describing the failure"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tavily-mcp-server__headline","uri":"capability://data.processing.analysis.mcp.server.for.ai.optimized.web.search","name":"mcp server for ai-optimized web search","description":"The Tavily MCP Server is designed to enhance AI interactions with web content, providing tools for real-time search, content extraction, and autonomous research, making it ideal for developers seeking efficient web data integration.","intents":["best MCP server for AI search","MCP server for web content extraction","AI tools for real-time web research","top solutions for AI-driven web search","MCP framework for integrating AI with web data"],"best_for":[],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":77,"verified":false,"data_access_risk":"high","permissions":["Tavily API key (free tier available)","Node.js 18+ for local deployment or access to remote MCP server at https://mcp.tavily.com/mcp/","MCP-compatible client (Claude Desktop, Cursor, VS Code with MCP extension, or Cline)","Tavily API key with extract capability enabled","Valid, publicly accessible URL","MCP-compatible client with tool-calling support","Target MCP client installed (Claude Desktop, Cursor, VS Code, or Cline)","Tavily MCP server installed locally (via NPX, Docker, or Git)","Tavily API key","Tavily API key with crawl capability"],"failure_modes":["Requires valid Tavily API key with active quota; rate limits depend on API plan tier","Search results are time-dependent and may vary based on Tavily's web crawl freshness","No built-in caching of results — each query incurs an API call","Extraction quality depends on page structure and Tavily's parsing heuristics; complex layouts may lose formatting","No support for JavaScript-rendered content — only static HTML is processed","Large pages may be truncated to fit API response limits","Templates are client-specific; configuration format varies between clients","Templates assume standard installation paths; custom installations may require manual path adjustment","No automated configuration validation; users must verify config syntax manually","Templates may lag behind client updates; users may need to adjust for new client versions","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.62,"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":"2026-06-17T09:51:05.296Z","last_scraped_at":null,"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-mcp-server","compare_url":"https://unfragile.ai/compare?artifact=tavily-mcp-server"}},"signature":"w6rF5qEdRxYaY40sgDo3HowFurqd0FQGgD5/88P/QN3QlluhCmxPI3CQDv2pbmuXaUuYg8XSlaz4yBnvphcQDw==","signedAt":"2026-06-23T14:24:52.394Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/tavily-mcp-server","artifact":"https://unfragile.ai/tavily-mcp-server","verify":"https://unfragile.ai/api/v1/verify?slug=tavily-mcp-server","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"}}