{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github_mcp-nickclyde-duckduckgo-mcp-server","slug":"mcp-nickclyde-duckduckgo-mcp-server","name":"duckduckgo-mcp-server","type":"mcp","url":"https://github.com/nickclyde/duckduckgo-mcp-server","page_url":"https://unfragile.ai/mcp-nickclyde-duckduckgo-mcp-server","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github_mcp-nickclyde-duckduckgo-mcp-server__cap_0","uri":"capability://search.retrieval.duckduckgo.web.search.with.llm.optimized.result.formatting","name":"duckduckgo web search with llm-optimized result formatting","description":"Executes web searches against DuckDuckGo's HTML interface and returns formatted results specifically optimized for LLM consumption. The implementation queries DuckDuckGo directly (avoiding API keys), parses HTML responses, removes ad content and redirect URLs, and structures results with titles, URLs, and snippets in a format that LLMs can easily consume and reason about. Rate limiting (30 req/min) prevents service abuse while maintaining responsiveness.","intents":["Enable an LLM agent to search the web for current information without API keys","Get up-to-date search results formatted for direct LLM context injection","Limit search result volume to avoid context window overflow while maintaining relevance","Prevent rate-limit blocking by implementing per-tool request throttling"],"best_for":["LLM application developers building Claude Desktop integrations","Teams building AI agents that need real-time web information","Developers avoiding third-party API key management for search functionality"],"limitations":["Rate limited to 30 requests/minute per tool instance, may throttle high-volume search scenarios","Depends on DuckDuckGo HTML interface stability — no official API contract, subject to breaking changes","Search result quality and freshness tied to DuckDuckGo's crawl frequency, not real-time indexing","No advanced search operators or filtering beyond basic query string (no site:, filetype:, etc.)"],"requires":["Python 3.9+","Network connectivity to DuckDuckGo (no proxy/VPN blocking)","MCP client compatible with FastMCP server (Claude Desktop, or custom MCP client)","requests library for HTTP operations"],"input_types":["text (search query string)","integer (max_results parameter, default 10)"],"output_types":["text (formatted string with titles, URLs, snippets)"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-nickclyde-duckduckgo-mcp-server__cap_1","uri":"capability://search.retrieval.webpage.content.fetching.and.html.to.text.parsing","name":"webpage content fetching and html-to-text parsing","description":"Retrieves full webpage content from a given URL and parses HTML into clean, LLM-readable text. The implementation uses HTTP requests to fetch raw HTML, applies HTML parsing and text extraction (removing scripts, styles, navigation elements), and formats the output for optimal LLM consumption. Rate limiting (20 req/min) prevents overwhelming target servers while maintaining throughput for content analysis workflows.","intents":["Extract article text from a search result URL for deeper analysis by an LLM","Convert webpage HTML into structured text for RAG or knowledge base ingestion","Retrieve full page content when search snippets are insufficient for decision-making","Batch-fetch multiple URLs with automatic rate limiting to avoid server blocking"],"best_for":["LLM agents performing multi-step research (search → fetch → analyze)","RAG systems needing to ingest full webpage content from URLs","Content analysis pipelines requiring clean text extraction from arbitrary websites"],"limitations":["Rate limited to 20 requests/minute, may bottleneck high-volume content fetching","No JavaScript execution — only parses static HTML, misses dynamically-loaded content","Respects robots.txt and server rate limits passively; no active crawl politeness negotiation","Large pages (>10MB) may cause memory pressure or timeout; no streaming or chunked parsing","Authentication-required pages (paywalls, login walls) return empty or error responses"],"requires":["Python 3.9+","Network connectivity to target websites","requests library for HTTP operations","HTML parsing library (BeautifulSoup or similar, bundled in dependencies)"],"input_types":["text (URL string)"],"output_types":["text (cleaned, formatted webpage content)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-nickclyde-duckduckgo-mcp-server__cap_2","uri":"capability://tool.use.integration.mcp.tool.registration.and.fastmcp.server.lifecycle.management","name":"mcp tool registration and fastmcp server lifecycle management","description":"Initializes and manages a FastMCP server instance that exposes search and content-fetching tools to MCP-compatible clients. The implementation uses FastMCP's @mcp.tool() decorator pattern to register callable Python functions as remote tools, handles tool invocation routing, manages server lifecycle (startup/shutdown), and provides error handling and logging. The server identifier 'ddg-search' enables client discovery and tool binding.","intents":["Register Python functions as callable tools accessible to Claude Desktop and other MCP clients","Manage server lifecycle (initialization, tool registration, shutdown) without manual boilerplate","Route tool invocations from MCP clients to appropriate Python functions with parameter validation","Enable seamless integration with Claude Desktop via MCP protocol without custom protocol implementation"],"best_for":["Developers building MCP servers that wrap existing Python libraries","Teams integrating Claude Desktop with custom backend services","Builders creating tool ecosystems where multiple MCP servers coordinate"],"limitations":["FastMCP abstracts MCP protocol details, limiting low-level protocol customization","Tool registration is static at server startup — no dynamic tool registration at runtime","Error handling relies on exception propagation; no built-in circuit breaker or fallback patterns","Single-threaded by default; concurrent tool invocations may block if tools perform I/O"],"requires":["Python 3.9+","FastMCP library (mcp package)","MCP client compatible with FastMCP server (Claude Desktop 0.1.0+, or custom MCP client)","Python async/await support for tool function definitions"],"input_types":["Python function signatures (tool definitions)"],"output_types":["MCP tool protocol messages (JSON-RPC over stdio)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-nickclyde-duckduckgo-mcp-server__cap_3","uri":"capability://safety.moderation.per.tool.rate.limiting.with.request.throttling","name":"per-tool rate limiting with request throttling","description":"Implements independent rate limiting for search (30 req/min) and content-fetching (20 req/min) tools using request throttling. The implementation tracks request timestamps per tool, enforces per-minute quotas, and delays requests that exceed limits to maintain compliance without rejecting calls. Rate limits are applied at the tool invocation layer, ensuring fairness across concurrent LLM clients and preventing service abuse.","intents":["Prevent rate-limit blocking from DuckDuckGo and target websites by staying within safe request thresholds","Distribute request budget fairly across multiple concurrent LLM agents or users","Avoid IP-based blocking by respecting server-side rate limits proactively","Provide predictable latency characteristics for LLM agents (bounded delay per request)"],"best_for":["Multi-user LLM applications where multiple agents may invoke search/fetch tools simultaneously","Production deployments requiring compliance with DuckDuckGo and website rate limits","Teams building long-running LLM agents that perform repeated searches over time"],"limitations":["Rate limits are per-server-instance, not global — multiple server instances bypass limits","No adaptive rate limiting based on server response codes (429, 503) — limits are static","Throttling adds latency (up to 2 seconds per request at max load) — not suitable for real-time applications","No priority queue or request batching — all requests treated equally regardless of importance","Rate limit state is in-memory; restarting server resets counters (no persistence)"],"requires":["Python 3.9+","Time-based request tracking (standard library time module)","Single-threaded or thread-safe request queue implementation"],"input_types":["tool invocation (implicit)"],"output_types":["delayed tool execution (transparent to caller)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-nickclyde-duckduckgo-mcp-server__cap_4","uri":"capability://data.processing.analysis.html.result.sanitization.and.ad.redirect.removal","name":"html result sanitization and ad/redirect removal","description":"Processes DuckDuckGo search results and fetched webpage content to remove advertisements, tracking redirects, and boilerplate elements. The implementation identifies and strips ad content from search results, cleans DuckDuckGo redirect URLs to expose actual target URLs, removes script/style tags and navigation elements from HTML, and formats remaining content for LLM consumption. This ensures LLMs receive clean, actionable information without noise.","intents":["Remove ads and sponsored content from search results so LLMs focus on organic results","Expose actual target URLs instead of DuckDuckGo redirect wrappers for direct access","Extract article body text from webpages while removing navigation, sidebars, and boilerplate","Improve LLM reasoning quality by reducing noise and irrelevant content in context"],"best_for":["LLM applications where clean, ad-free search results improve response quality","Content analysis pipelines requiring boilerplate-free text extraction","Teams building research agents that need to distinguish organic results from sponsored content"],"limitations":["Sanitization rules are heuristic-based (pattern matching) — may over-strip legitimate content or miss ads","No machine learning-based content classification — relies on HTML structure and CSS classes","Webpage structure varies widely; generic boilerplate removal may fail on unusual layouts","Dynamic content (JavaScript-rendered ads) is not detected or removed","Sanitization rules may become outdated if DuckDuckGo or target websites change HTML structure"],"requires":["Python 3.9+","HTML parsing library (BeautifulSoup or similar)","Regex support for URL pattern matching"],"input_types":["HTML string (from DuckDuckGo or target website)"],"output_types":["cleaned text string (ads/boilerplate removed)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-nickclyde-duckduckgo-mcp-server__cap_5","uri":"capability://tool.use.integration.claude.desktop.integration.via.mcp.protocol","name":"claude desktop integration via mcp protocol","description":"Enables the DuckDuckGo MCP server to integrate with Claude Desktop through the Model Context Protocol, allowing Claude to invoke search and content-fetching tools directly. The implementation exposes the FastMCP server over stdio (standard input/output), implements MCP protocol message handling (JSON-RPC), and registers tools in Claude Desktop's configuration. This provides seamless tool access without custom UI or API management.","intents":["Enable Claude Desktop to search the web and fetch webpage content natively","Provide Claude with real-time information access without leaving the chat interface","Integrate custom backend services (DuckDuckGo search) into Claude's tool ecosystem","Configure tool availability per Claude Desktop instance via config file"],"best_for":["Claude Desktop users wanting web search capabilities in conversations","Teams deploying Claude Desktop with custom tool integrations","Developers building Claude Desktop extensions via MCP protocol"],"limitations":["Requires Claude Desktop 0.1.0+ with MCP support — not compatible with Claude web interface","Configuration is per-machine (claude_desktop_config.json) — not centrally managed","Tool invocation is synchronous; long-running searches block Claude's response generation","No authentication or access control — any Claude Desktop instance with config can invoke tools","Tool output is limited by Claude's context window; large fetched pages may be truncated"],"requires":["Claude Desktop 0.1.0+","Python 3.9+ with FastMCP server running","claude_desktop_config.json configuration file with MCP server entry","Network connectivity between Claude Desktop and MCP server (local or remote)"],"input_types":["MCP tool invocation (from Claude Desktop)"],"output_types":["MCP tool result (JSON-RPC response)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-nickclyde-duckduckgo-mcp-server__cap_6","uri":"capability://automation.workflow.multi.deployment.option.support.smithery.pip.docker","name":"multi-deployment option support (smithery, pip, docker)","description":"Provides multiple installation and deployment pathways for the DuckDuckGo MCP server: Smithery (simplified MCP server registry), pip package installation, and Docker containerization. Each deployment method handles dependency management, environment configuration, and server lifecycle differently, enabling developers to choose based on their infrastructure and operational preferences. Deployment options are documented with setup instructions for each method.","intents":["Install MCP server via Smithery registry for one-click Claude Desktop integration","Deploy as Python package for custom environments or local development","Containerize server for cloud deployment, multi-instance scaling, or isolated environments","Choose deployment method based on existing infrastructure (local dev, Docker, Kubernetes)"],"best_for":["Individual developers wanting quick Claude Desktop setup (Smithery)","Teams with Python-centric development workflows (pip install)","Organizations deploying to cloud/Kubernetes (Docker)"],"limitations":["Smithery deployment requires Smithery registry account and approval process","pip installation requires Python 3.9+ and manual dependency management","Docker deployment adds container overhead (~100MB image size) and requires Docker runtime","No Kubernetes manifests or Helm charts provided — Docker image only","Deployment documentation may lag behind code changes in active development"],"requires":["Smithery: Smithery CLI and account","pip: Python 3.9+, pip package manager","Docker: Docker runtime, Docker image build capability"],"input_types":["deployment configuration (environment-specific)"],"output_types":["running MCP server instance"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-nickclyde-duckduckgo-mcp-server__cap_7","uri":"capability://safety.moderation.comprehensive.error.handling.and.result.formatting","name":"comprehensive error handling and result formatting","description":"Implements error handling across search and content-fetching operations with graceful degradation and informative error messages. The implementation catches network errors, parsing failures, rate-limit violations, and malformed inputs, returning structured error responses that LLMs can interpret and act upon. Result formatting ensures consistent output structure (titles, URLs, snippets for search; cleaned text for content) regardless of input variation.","intents":["Handle network failures gracefully without crashing the MCP server","Provide LLMs with actionable error information (e.g., 'URL unreachable' vs 'rate limited')","Format search results and fetched content consistently so LLMs can parse results reliably","Prevent malformed inputs from causing parsing errors or security issues"],"best_for":["Production LLM applications requiring robustness against network failures","Teams building agents that need to handle tool failures gracefully","Applications where consistent result formatting improves LLM reasoning"],"limitations":["Error messages are text-based; no structured error codes for programmatic handling","Timeout handling may be too aggressive (default timeout may be <5s) for slow networks","No retry logic — transient failures (network blips) are reported as permanent errors","Malformed HTML may cause parsing errors that are not caught; no fallback to raw HTML","Error messages are not localized; only English error text provided"],"requires":["Python 3.9+","Exception handling in tool functions","Structured logging (optional, for debugging)"],"input_types":["search query or URL (may be malformed)"],"output_types":["formatted result string or error message"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":42,"verified":false,"data_access_risk":"moderate","permissions":["Python 3.9+","Network connectivity to DuckDuckGo (no proxy/VPN blocking)","MCP client compatible with FastMCP server (Claude Desktop, or custom MCP client)","requests library for HTTP operations","Network connectivity to target websites","HTML parsing library (BeautifulSoup or similar, bundled in dependencies)","FastMCP library (mcp package)","MCP client compatible with FastMCP server (Claude Desktop 0.1.0+, or custom MCP client)","Python async/await support for tool function definitions","Time-based request tracking (standard library time module)"],"failure_modes":["Rate limited to 30 requests/minute per tool instance, may throttle high-volume search scenarios","Depends on DuckDuckGo HTML interface stability — no official API contract, subject to breaking changes","Search result quality and freshness tied to DuckDuckGo's crawl frequency, not real-time indexing","No advanced search operators or filtering beyond basic query string (no site:, filetype:, etc.)","Rate limited to 20 requests/minute, may bottleneck high-volume content fetching","No JavaScript execution — only parses static HTML, misses dynamically-loaded content","Respects robots.txt and server rate limits passively; no active crawl politeness negotiation","Large pages (>10MB) may cause memory pressure or timeout; no streaming or chunked parsing","Authentication-required pages (paywalls, login walls) return empty or error responses","FastMCP abstracts MCP protocol details, limiting low-level protocol customization","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.44283548057240385,"quality":0.41,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.75,"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":"2026-05-24T12:16:22.065Z","last_scraped_at":"2026-05-03T14:23:34.856Z","last_commit":"2026-05-01T00:39:58Z"},"community":{"stars":1068,"forks":159,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mcp-nickclyde-duckduckgo-mcp-server","compare_url":"https://unfragile.ai/compare?artifact=mcp-nickclyde-duckduckgo-mcp-server"}},"signature":"DZmCVLHvw0dcHj8DQ+KGyyTvAFLB4ZPRDds9BHULLj1YorLHYPPywhWKlKT7Sudvmrdsr/rpbO9EPJvUWFyiBw==","signedAt":"2026-06-20T22:57:15.646Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-nickclyde-duckduckgo-mcp-server","artifact":"https://unfragile.ai/mcp-nickclyde-duckduckgo-mcp-server","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-nickclyde-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"}}