{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"duckduckgo-mcp-server","slug":"duckduckgo-mcp-server","name":"DuckDuckGo MCP Server","type":"mcp","url":"https://github.com/nickclyde/duckduckgo-mcp-server","page_url":"https://unfragile.ai/duckduckgo-mcp-server","categories":["mcp-servers"],"tags":["duckduckgo","search","privacy","community"],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"duckduckgo-mcp-server__cap_0","uri":"capability://search.retrieval.duckduckgo.backed.web.search.with.llm.optimized.result.formatting","name":"duckduckgo-backed web search with llm-optimized result formatting","description":"Executes web searches against DuckDuckGo's HTML interface (not API-dependent) and returns formatted results with titles, URLs, and snippets cleaned for LLM consumption. The search tool implements query parameter handling with configurable max_results (default 10) and applies post-processing to remove ads and clean redirect URLs before returning structured text output. Built on FastMCP framework's @mcp.tool() decorator pattern for seamless MCP protocol integration.","intents":["I need my LLM agent to search the web for current information without API keys","I want search results formatted specifically for LLM context windows, not human browsing","I need to add web search capability to Claude Desktop without external API dependencies"],"best_for":["LLM application developers building agents that need real-time web information","Privacy-conscious teams avoiding API-tracked search services","Claude Desktop users wanting integrated search without API key management"],"limitations":["Depends on DuckDuckGo's HTML interface stability — no official API contract means potential breaking changes","Rate limited to 30 requests/minute for search operations to prevent service abuse","No advanced search operators or filtering beyond basic query string (DuckDuckGo HTML interface limitation)","Results quality depends on DuckDuckGo's ranking algorithm, not customizable"],"requires":["Python 3.9+","Network connectivity to DuckDuckGo (no API key required)","MCP-compatible client (Claude Desktop, or any MCP host)","FastMCP framework (included in package dependencies)"],"input_types":["text (search query string)","integer (max_results parameter, 1-100)"],"output_types":["text (formatted search results with titles, URLs, snippets)"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"duckduckgo-mcp-server__cap_1","uri":"capability://data.processing.analysis.webpage.content.fetching.and.html.to.text.parsing","name":"webpage content fetching and html-to-text parsing","description":"Retrieves raw HTML from specified URLs and parses it into cleaned, LLM-friendly text content using HTML parsing libraries. The fetch_content tool accepts a URL parameter, handles HTTP requests with error management, strips HTML markup, removes boilerplate (navigation, ads, scripts), and returns structured text suitable for LLM context injection. Implements rate limiting (20 requests/minute) and comprehensive error handling for network failures, invalid URLs, and parsing exceptions.","intents":["I need to extract readable text from a webpage URL found in search results","I want my LLM to read full article content, not just search snippets","I need to parse web content without managing HTTP clients or HTML parsing libraries myself"],"best_for":["LLM agents that need to read full webpage content after search discovery","Research automation workflows combining search + content extraction","Multi-step reasoning tasks where LLMs need detailed source material"],"limitations":["Rate limited to 20 requests/minute for content fetching to prevent overload","Cannot handle JavaScript-rendered content (static HTML parsing only) — dynamic SPAs will return incomplete data","No support for authentication-required pages or paywalled content","Large pages (>10MB) may timeout or be truncated; no streaming support","PDF, video, and binary content types not supported — text extraction only"],"requires":["Python 3.9+","Network connectivity to target URLs","MCP-compatible client","HTML parsing library (BeautifulSoup or similar, included in dependencies)"],"input_types":["text (URL string, must be valid HTTP/HTTPS)"],"output_types":["text (cleaned, parsed webpage content)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"duckduckgo-mcp-server__cap_2","uri":"capability://automation.workflow.rate.limited.request.throttling.with.per.tool.quotas","name":"rate-limited request throttling with per-tool quotas","description":"Implements token-bucket style rate limiting with separate quotas for search (30 req/min) and content fetching (20 req/min) operations. The rate limiter tracks request timestamps and enforces delays or rejections when quotas are exceeded, preventing service abuse and DuckDuckGo overload. Built into the tool execution pipeline before external requests are made, with error responses returned to the MCP client when limits are hit.","intents":["I need to prevent my LLM agent from hammering DuckDuckGo and getting IP-blocked","I want different rate limits for search vs content fetching based on service impact","I need transparent rate limit feedback so my agent can handle throttling gracefully"],"best_for":["Production LLM agents running continuously or in high-volume scenarios","Teams sharing a single MCP server instance across multiple clients","Responsible service consumers wanting to avoid IP bans or service degradation"],"limitations":["Rate limits are per-server-instance, not distributed across multiple server instances — no shared state across deployments","No adaptive rate limiting based on DuckDuckGo response codes or server load","Quota resets on server restart — no persistent rate limit state across restarts","No per-client or per-user rate limiting — all requests share the same quota pool","Hard rejection when quota exceeded; no queue or backoff retry mechanism built-in"],"requires":["Python 3.9+","In-memory state tracking (no external rate limit service required)","MCP server instance running continuously"],"input_types":["implicit (rate limiting applied to all search and fetch_content requests)"],"output_types":["error response (when rate limit exceeded) or normal tool output (when within quota)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"duckduckgo-mcp-server__cap_3","uri":"capability://tool.use.integration.mcp.protocol.server.implementation.with.fastmcp.framework","name":"mcp protocol server implementation with fastmcp framework","description":"Implements the Model Context Protocol (MCP) specification using the FastMCP framework, exposing search and content fetching as standardized MCP tools with schema validation, error handling, and protocol-compliant request/response serialization. The server initializes as a FastMCP instance with identifier 'ddg-search', decorates tool methods with @mcp.tool(), and handles MCP client communication including tool discovery, invocation, and result formatting. Supports multiple deployment modes (Smithery, Python package, Docker) with standardized MCP configuration.","intents":["I want to integrate DuckDuckGo search into Claude Desktop or other MCP-compatible clients","I need a standardized tool interface that any MCP client can discover and invoke","I want to deploy this server without custom integration code for each client"],"best_for":["Claude Desktop users wanting native search integration","Developers building MCP-compatible LLM applications","Teams standardizing on MCP protocol for tool composition"],"limitations":["MCP protocol overhead adds ~50-100ms latency per tool invocation vs direct function calls","Requires MCP-compatible client — not usable with standard REST API clients or direct Python imports","Tool schema validation adds serialization/deserialization overhead for large result sets","No built-in authentication or authorization — relies on client-side access control"],"requires":["Python 3.9+","FastMCP framework (included in dependencies)","MCP-compatible client (Claude Desktop, custom MCP host, etc.)","Network connectivity between client and server"],"input_types":["MCP tool invocation requests (JSON-RPC format)"],"output_types":["MCP tool response (JSON-RPC format with results or errors)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"duckduckgo-mcp-server__cap_4","uri":"capability://automation.workflow.multi.deployment.packaging.with.smithery.pip.and.docker.support","name":"multi-deployment packaging with smithery, pip, and docker support","description":"Provides three deployment pathways: Smithery (simplified MCP server registry installation), Python pip package installation, and Docker containerization. Each deployment method maintains identical MCP tool interface and functionality while accommodating different infrastructure preferences. Smithery integration enables one-click installation in Claude Desktop; pip allows local Python environment installation; Docker enables containerized deployment with environment isolation. Configuration is standardized across all deployment modes via environment variables and MCP configuration files.","intents":["I want to install this in Claude Desktop with minimal setup","I need to run this in a containerized environment for production","I want to install this as a Python package in my existing environment"],"best_for":["Claude Desktop users wanting one-click installation (Smithery)","Python developers integrating into existing environments (pip)","DevOps teams deploying to Kubernetes or containerized infrastructure (Docker)"],"limitations":["Smithery deployment limited to Claude Desktop integration — not suitable for standalone servers","Docker deployment adds container overhead (~100-200MB image size, startup latency)","pip installation requires Python 3.9+ and pip package manager — not suitable for non-Python environments","Configuration differs slightly across deployment modes (environment variables vs config files) — potential inconsistency"],"requires":["For Smithery: Claude Desktop 0.1.0+","For pip: Python 3.9+, pip package manager","For Docker: Docker runtime, container registry access (optional)"],"input_types":["implicit (deployment configuration)"],"output_types":["running MCP server instance"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"duckduckgo-mcp-server__cap_5","uri":"capability://safety.moderation.error.handling.and.graceful.degradation.with.comprehensive.exception.management","name":"error handling and graceful degradation with comprehensive exception management","description":"Implements multi-layer error handling covering network failures (connection timeouts, DNS resolution), invalid inputs (malformed URLs, empty queries), parsing failures (corrupted HTML, encoding issues), and rate limit violations. Each error type is caught, logged, and returned to the MCP client with descriptive error messages rather than crashing the server. Includes fallback behaviors such as partial result return on parsing failures and clear error codes for client-side retry logic.","intents":["I need my LLM agent to handle search/fetch failures gracefully without crashing","I want clear error messages so my agent can decide whether to retry or use alternative strategies","I need the server to stay running even when individual requests fail"],"best_for":["Production LLM agents requiring high availability","Multi-step reasoning workflows where individual search failures shouldn't halt execution","Teams building resilient agent systems with fallback strategies"],"limitations":["Error messages are returned to client but not persisted — no error logging to external systems by default","No automatic retry logic — client must implement retry strategy","Timeout handling is fixed (no configurable timeout values) — may be too aggressive or lenient for some use cases","Partial results on parsing failures may be incomplete or misleading without clear indication of what was lost"],"requires":["Python 3.9+","MCP-compatible client that can handle error responses"],"input_types":["implicit (applied to all tool invocations)"],"output_types":["error response (MCP error format) or partial/empty results on failure"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"duckduckgo-mcp-server__headline","uri":"capability://search.retrieval.duckduckgo.mcp.server.for.web.search.integration","name":"duckduckgo mcp server for web search integration","description":"A community-driven Model Context Protocol (MCP) server that enables seamless web search and content fetching through DuckDuckGo, prioritizing user privacy without requiring API keys.","intents":["best MCP server for web search","MCP server for LLM integration","DuckDuckGo search API alternatives","privacy-focused search server","community MCP server for developers"],"best_for":["developers seeking privacy-focused search solutions"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":59,"verified":false,"data_access_risk":"moderate","permissions":["Python 3.9+","Network connectivity to DuckDuckGo (no API key required)","MCP-compatible client (Claude Desktop, or any MCP host)","FastMCP framework (included in package dependencies)","Network connectivity to target URLs","MCP-compatible client","HTML parsing library (BeautifulSoup or similar, included in dependencies)","In-memory state tracking (no external rate limit service required)","MCP server instance running continuously","FastMCP framework (included in dependencies)"],"failure_modes":["Depends on DuckDuckGo's HTML interface stability — no official API contract means potential breaking changes","Rate limited to 30 requests/minute for search operations to prevent service abuse","No advanced search operators or filtering beyond basic query string (DuckDuckGo HTML interface limitation)","Results quality depends on DuckDuckGo's ranking algorithm, not customizable","Rate limited to 20 requests/minute for content fetching to prevent overload","Cannot handle JavaScript-rendered content (static HTML parsing only) — dynamic SPAs will return incomplete data","No support for authentication-required pages or paywalled content","Large pages (>10MB) may timeout or be truncated; no streaming support","PDF, video, and binary content types not supported — text extraction only","Rate limits are per-server-instance, not distributed across multiple server instances — no shared state across deployments","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.8500000000000001,"ecosystem":0.52,"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":"2026-06-17T09:51:04.691Z","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=duckduckgo-mcp-server","compare_url":"https://unfragile.ai/compare?artifact=duckduckgo-mcp-server"}},"signature":"8L0Geoo6lY8CbXQs0GYEnf24RYXGceXckuu+alDtI6VK6HMvJfBFelzUjuOHKbXy2dgERjqlA4GqZOFBfAzUDg==","signedAt":"2026-06-21T20:05:26.683Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/duckduckgo-mcp-server","artifact":"https://unfragile.ai/duckduckgo-mcp-server","verify":"https://unfragile.ai/api/v1/verify?slug=duckduckgo-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"}}