{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-web-search-mcp","slug":"web-search-mcp","name":"Web Search MCP","type":"mcp","url":"https://github.com/mrkrsl/web-search-mcp","page_url":"https://unfragile.ai/web-search-mcp","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-web-search-mcp__cap_0","uri":"capability://search.retrieval.multi.engine.web.search.with.automatic.fallback.cascading","name":"multi-engine web search with automatic fallback cascading","description":"Performs web searches across three independent search engines (Bing, Brave, DuckDuckGo) with automatic cascading fallback when primary engines fail or return insufficient results. The system queries engines sequentially, aggregating results and applying quality assessment filters to ensure relevance before returning up to 10 ranked results. This architecture eliminates single points of failure inherent in API-dependent search solutions.","intents":["I need to search the web without relying on a single search engine API that might be rate-limited or unavailable","I want search results that are resilient to individual search engine outages or blocks","I need to integrate web search into my local LLM without external API dependencies"],"best_for":["developers building local-first AI agents","teams deploying LLMs in air-gapped or restricted network environments","builders avoiding third-party API dependencies for cost or compliance reasons"],"limitations":["No built-in deduplication across engines — may return similar results from multiple sources","Search quality depends on engine availability and current blocking status — no guaranteed coverage","Cascading fallback adds latency (sequential engine queries rather than parallel) — typical 2-5 second response time","No support for advanced search operators or engine-specific syntax — limited to basic keyword queries"],"requires":["Node.js 18+","Network access to Bing, Brave, and DuckDuckGo search endpoints","MCP client compatible with stdio-based protocol (Claude Desktop, LM Studio, or custom implementation)"],"input_types":["text (search query string)"],"output_types":["structured JSON with array of search results containing title, URL, snippet, and relevance metadata"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-web-search-mcp__cap_1","uri":"capability://data.processing.analysis.concurrent.full.page.content.extraction.with.dual.strategy.fallback","name":"concurrent full-page content extraction with dual-strategy fallback","description":"Extracts complete page content from multiple search result URLs concurrently using a two-tier strategy: fast HTTP requests with cheerio-based HTML parsing as primary method, automatically falling back to Playwright browser automation for JavaScript-heavy or dynamically-rendered pages. The system manages a pool of up to 3 browser instances with health checking to prevent resource exhaustion while maintaining extraction reliability across diverse page types.","intents":["I need to extract full article text and structured content from search results, not just snippets","I want extraction that works on both static HTML and JavaScript-rendered pages without manual fallback logic","I need to process multiple pages in parallel without spawning unlimited browser instances"],"best_for":["LLM agents that need comprehensive page context for reasoning and summarization","research tools requiring full article extraction from diverse sources","teams building RAG systems that need high-quality document content from web sources"],"limitations":["Browser pool limited to 3 concurrent instances — extraction queues if more than 3 pages requested simultaneously","JavaScript execution adds 1-3 seconds per page for fallback cases — significantly slower than HTTP-only extraction","Memory overhead of Playwright instances — each browser consumes ~100-150MB, limiting total concurrent extractions","No handling of authentication-required pages or pages behind paywalls — extraction fails silently on protected content","Extracted content may include navigation elements, ads, or boilerplate not filtered by default — requires post-processing for clean text"],"requires":["Node.js 18+","Playwright browser binaries (auto-installed via npm, ~200MB disk space)","Network access to target websites","Sufficient system memory for browser pool (minimum 512MB free RAM recommended)"],"input_types":["array of URLs (strings)"],"output_types":["structured JSON with extracted page content, metadata (title, description), and extraction method used (http or browser)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-web-search-mcp__cap_10","uri":"capability://code.generation.editing.typescript.type.system.with.schema.validation.for.tool.parameters","name":"typescript type system with schema validation for tool parameters","description":"Defines strict TypeScript types for all tool parameters, search results, and extracted content, with runtime schema validation to ensure MCP clients send correctly-formatted requests. The type system includes interfaces for search results, page content, extraction metadata, and configuration, enabling type-safe tool invocation and IDE autocomplete for client developers. Schema validation prevents malformed requests from reaching the extraction pipeline.","intents":["I want type-safe tool invocation with IDE autocomplete when building MCP clients","I need validation to ensure clients send correctly-formatted requests","I want clear documentation of tool parameters and return types"],"best_for":["TypeScript developers building MCP clients that use web-search-mcp","teams that value type safety and IDE support in tool integration","builders creating custom MCP clients with strict parameter validation"],"limitations":["Type system is TypeScript-only — non-TypeScript clients must implement validation separately","Schema validation adds ~10-50ms overhead per request","Type definitions must be manually kept in sync with implementation — no automatic generation","Runtime validation only checks structure, not semantic correctness (e.g., valid URLs, reasonable timeouts)","No JSON Schema export — clients cannot automatically generate types from schemas"],"requires":["TypeScript 4.5+ (for TypeScript clients)","MCP client that supports typed tool parameters"],"input_types":["JSON-RPC requests with typed parameters"],"output_types":["validated parameters, type-safe results"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-web-search-mcp__cap_2","uri":"capability://search.retrieval.lightweight.search.only.mode.with.snippet.extraction","name":"lightweight search-only mode with snippet extraction","description":"Provides a performance-optimized search tool that returns only search engine snippets (titles, URLs, and brief descriptions) without extracting full page content. This tool uses the same multi-engine search infrastructure as the full-search capability but skips the content extraction pipeline entirely, reducing latency by 80-90% and eliminating browser resource consumption. Includes explicit browser cleanup to prevent resource leaks in long-running agent scenarios.","intents":["I need quick search results to assess relevance before committing to full content extraction","I want to minimize latency and resource usage for rapid research or fact-checking","I need search results for an LLM to decide which sources to investigate further"],"best_for":["rapid prototyping and testing of search-based agents","resource-constrained environments (edge devices, serverless functions)","multi-step agent workflows where search is one of many tools and latency matters"],"limitations":["Returns only search engine snippets — insufficient for detailed content analysis or direct citation","Snippet quality varies by search engine — some engines provide minimal descriptions","No content extraction means LLM cannot verify claims or access full context from sources","Limited to 5-10 results by default — may miss relevant sources beyond first page"],"requires":["Node.js 18+","Network access to search engines","MCP client compatible with stdio protocol"],"input_types":["text (search query string)"],"output_types":["structured JSON array with title, URL, and snippet for each result"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-web-search-mcp__cap_3","uri":"capability://data.processing.analysis.targeted.single.page.content.extraction.with.format.preservation","name":"targeted single-page content extraction with format preservation","description":"Extracts and returns the complete content from a single specified URL, applying the same dual-strategy extraction pipeline (HTTP+cheerio first, Playwright fallback) as the full-search tool but optimized for direct URL input rather than search results. Preserves page structure, metadata (title, description, author), and content formatting while filtering common boilerplate elements. Useful for agents that need to investigate specific URLs discovered through other means.","intents":["I need to extract content from a specific URL that was mentioned or discovered outside of search results","I want to verify or deep-dive into a particular source that an LLM or user has identified","I need to extract and process a single page without running a full search first"],"best_for":["agents that need to follow links or investigate specific URLs","workflows where URLs are provided directly rather than discovered via search","content processing pipelines that need per-URL extraction control"],"limitations":["Single URL only — no batch processing of multiple URLs in one call","Same browser pool constraints as full-search — extraction queues if called rapidly in succession","No automatic link following or crawling — only extracts the specified URL","Boilerplate filtering is heuristic-based — may remove legitimate content on unusual page layouts","No support for authentication or cookies — cannot extract from pages requiring login"],"requires":["Node.js 18+","Valid, publicly-accessible URL","Network access to target website","Playwright browser binaries"],"input_types":["text (single URL string)"],"output_types":["structured JSON with extracted content, metadata, and extraction method"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-web-search-mcp__cap_4","uri":"capability://automation.workflow.browser.pool.management.with.health.checking.and.resource.limits","name":"browser pool management with health checking and resource limits","description":"Manages a configurable pool of up to 3 Playwright browser instances with automatic health checking, graceful cleanup, and resource leak prevention. The pool implements queue-based request scheduling to prevent browser exhaustion, monitors instance health (detecting crashed or unresponsive browsers), and automatically restarts failed instances. This infrastructure enables concurrent content extraction across multiple pages while maintaining predictable resource consumption in long-running agent scenarios.","intents":["I need to extract content from multiple pages concurrently without spawning unlimited browser processes","I want to prevent memory leaks and browser crashes in long-running agent applications","I need predictable resource consumption when running extraction-heavy workflows"],"best_for":["production LLM agents that run continuously and make many extraction requests","resource-constrained environments where browser overhead must be minimized","teams building reliable web-scraping infrastructure for AI systems"],"limitations":["Fixed pool size of 3 browsers — cannot scale beyond 3 concurrent extractions without queuing","Health checking adds ~100-200ms overhead per extraction request","Browser restart on failure causes 1-2 second delay for affected requests","No persistent state across browser instances — each extraction is independent","Memory consumption is fixed at ~300-450MB for full pool regardless of usage"],"requires":["Node.js 18+","Sufficient system memory (minimum 512MB free RAM for full pool)","Playwright browser binaries installed"],"input_types":["internal (managed by extraction tools)"],"output_types":["internal (browser instances provided to extraction pipeline)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-web-search-mcp__cap_5","uri":"capability://data.processing.analysis.quality.assessment.and.relevance.filtering.for.search.results","name":"quality assessment and relevance filtering for search results","description":"Applies configurable quality filters to search results after aggregation from multiple engines, assessing relevance based on query-to-result similarity, content length, and domain reputation heuristics. The system ranks results by relevance score and filters out low-quality matches before returning to the client. Quality thresholds are configurable via environment variables, allowing tuning for different use cases (strict filtering for research vs. permissive for exploration).","intents":["I want search results filtered to remove spam, low-quality, or irrelevant pages","I need to configure quality thresholds based on my specific use case (research vs. exploration)","I want results ranked by relevance rather than just search engine order"],"best_for":["agents that need high-quality search results for reasoning and decision-making","research workflows where result quality directly impacts output quality","teams that want to tune search behavior for domain-specific use cases"],"limitations":["Quality assessment is heuristic-based — may filter legitimate results or pass low-quality ones","Relevance scoring does not understand semantic meaning — relies on keyword matching and length heuristics","Domain reputation heuristics are static — cannot adapt to newly-created or emerging sources","No machine learning-based quality ranking — purely rule-based filtering","Filtering may be too aggressive or permissive depending on query type and domain"],"requires":["Node.js 18+","Environment variables for quality threshold configuration (optional — defaults provided)"],"input_types":["internal (applied to search results after aggregation)"],"output_types":["filtered and ranked search results with relevance scores"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-web-search-mcp__cap_6","uri":"capability://tool.use.integration.mcp.protocol.server.implementation.with.stdio.based.json.rpc.communication","name":"mcp protocol server implementation with stdio-based json-rpc communication","description":"Implements the Model Context Protocol (MCP) as a TypeScript server that communicates with MCP clients (Claude Desktop, LM Studio, custom implementations) via JSON-RPC over stdin/stdout. The server exposes three tools (full-web-search, get-web-search-summaries, get-single-web-page-content) as MCP resources with typed schemas, enabling seamless integration with any MCP-compatible client without custom integration code. Handles protocol versioning, error responses, and graceful shutdown.","intents":["I want to integrate web search capabilities into Claude Desktop or LM Studio without custom plugins","I need a standardized way to expose web search as a tool to any MCP-compatible LLM client","I want to build a local search server that works with multiple LLM applications"],"best_for":["developers integrating web search into Claude Desktop or LM Studio","teams building custom MCP clients that need web search capabilities","builders creating local-first LLM applications with standardized tool interfaces"],"limitations":["MCP protocol is still evolving — compatibility may break with future client updates","stdio-based communication adds overhead compared to direct library imports — ~50-100ms per request","No built-in authentication or authorization — assumes trusted local client","Limited to tools exposed via MCP schema — cannot expose arbitrary functions","Error handling relies on JSON-RPC error codes — may not provide detailed debugging information"],"requires":["Node.js 18+","MCP-compatible client (Claude Desktop, LM Studio, or custom implementation)","stdio communication channel between client and server"],"input_types":["JSON-RPC requests with tool name and parameters"],"output_types":["JSON-RPC responses with tool results or error codes"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-web-search-mcp__cap_7","uri":"capability://automation.workflow.environment.variable.based.configuration.for.timeouts.thresholds.and.resource.limits","name":"environment variable-based configuration for timeouts, thresholds, and resource limits","description":"Provides extensive configurability through environment variables for search timeouts, content extraction timeouts, quality thresholds, browser pool size, result limits, and rate limiting parameters. Configuration is applied at startup and affects all subsequent requests, enabling operators to tune the server for different deployment scenarios (low-latency vs. comprehensive, resource-constrained vs. unlimited) without code changes. Includes sensible defaults for all parameters.","intents":["I need to tune search and extraction timeouts for my network conditions and hardware","I want to limit resource consumption in constrained environments (edge devices, serverless)","I need to configure quality thresholds and result limits for my specific use case"],"best_for":["operators deploying web-search-mcp in diverse environments (cloud, edge, local)","teams that need to tune behavior without rebuilding or redeploying code","builders creating containerized or serverless deployments with environment-based configuration"],"limitations":["Configuration is static at startup — cannot change parameters without restarting server","No validation of configuration values — invalid settings may cause silent failures or unexpected behavior","Limited documentation of configuration options — requires reading source code to discover all parameters","No configuration file support — only environment variables (no .env file parsing)","No per-request configuration overrides — all requests use the same global configuration"],"requires":["Node.js 18+","Knowledge of available environment variables (documented in README or source code)"],"input_types":["environment variables (strings, numbers)"],"output_types":["internal (affects server behavior)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-web-search-mcp__cap_8","uri":"capability://automation.workflow.rate.limiting.and.request.queuing.for.search.engine.protection","name":"rate limiting and request queuing for search engine protection","description":"Implements configurable rate limiting to prevent overwhelming search engines with rapid requests, using request queuing and per-engine throttling. The system tracks request rates per search engine and delays requests if thresholds are exceeded, preventing IP blocking or temporary bans. Rate limits are configurable via environment variables and can be tuned based on deployment requirements and search engine policies.","intents":["I need to avoid getting IP-blocked by search engines due to rapid requests","I want to respect search engine rate limits and terms of service","I need to queue requests gracefully when rate limits are exceeded"],"best_for":["production deployments that make many search requests over time","teams concerned about search engine blocking or IP bans","agents that need to handle burst request loads without overwhelming search engines"],"limitations":["Rate limiting adds latency to requests — may queue requests for 1-5 seconds if limits are exceeded","No distributed rate limiting — limits are per-server instance, not across multiple deployments","Search engines may still block based on other signals (user-agent, request patterns) even with rate limiting","No adaptive rate limiting — limits are static, not adjusted based on search engine responses","Queued requests may timeout if queue depth exceeds configured limits"],"requires":["Node.js 18+","Environment variables for rate limit configuration (optional)"],"input_types":["internal (applied to all search requests)"],"output_types":["delayed or queued search requests"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-web-search-mcp__cap_9","uri":"capability://automation.workflow.error.handling.and.graceful.degradation.across.extraction.failures","name":"error handling and graceful degradation across extraction failures","description":"Implements multi-level error handling that gracefully degrades when individual extraction attempts fail: if HTTP extraction fails, automatically falls back to Playwright; if a single page extraction fails, continues processing other pages rather than failing the entire request; if a search engine is unavailable, cascades to the next engine. Errors are logged with context but do not block the overall operation, allowing partial results to be returned even when some components fail.","intents":["I need search and extraction to continue working even when some pages or engines fail","I want partial results rather than complete failure when individual extractions fail","I need visibility into what failed and why without the entire operation failing"],"best_for":["production agents that need reliability over perfection","workflows where partial results are better than complete failure","teams building resilient systems that must handle unreliable web resources"],"limitations":["Graceful degradation may return incomplete or partial results without clear indication of what failed","Error logging is internal — clients may not know which specific pages or engines failed","Fallback mechanisms add latency — HTTP failures trigger browser fallback which takes 1-3 seconds","No retry logic with exponential backoff — failed requests are not retried","Silent failures may mask underlying issues — operators may not notice systematic problems"],"requires":["Node.js 18+","Logging infrastructure to capture error details"],"input_types":["internal (applied to all extraction and search operations)"],"output_types":["partial results with graceful degradation, error logging"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":32,"verified":false,"data_access_risk":"moderate","permissions":["Node.js 18+","Network access to Bing, Brave, and DuckDuckGo search endpoints","MCP client compatible with stdio-based protocol (Claude Desktop, LM Studio, or custom implementation)","Playwright browser binaries (auto-installed via npm, ~200MB disk space)","Network access to target websites","Sufficient system memory for browser pool (minimum 512MB free RAM recommended)","TypeScript 4.5+ (for TypeScript clients)","MCP client that supports typed tool parameters","Network access to search engines","MCP client compatible with stdio protocol"],"failure_modes":["No built-in deduplication across engines — may return similar results from multiple sources","Search quality depends on engine availability and current blocking status — no guaranteed coverage","Cascading fallback adds latency (sequential engine queries rather than parallel) — typical 2-5 second response time","No support for advanced search operators or engine-specific syntax — limited to basic keyword queries","Browser pool limited to 3 concurrent instances — extraction queues if more than 3 pages requested simultaneously","JavaScript execution adds 1-3 seconds per page for fallback cases — significantly slower than HTTP-only extraction","Memory overhead of Playwright instances — each browser consumes ~100-150MB, limiting total concurrent extractions","No handling of authentication-required pages or pages behind paywalls — extraction fails silently on protected content","Extracted content may include navigation elements, ads, or boilerplate not filtered by default — requires post-processing for clean text","Type system is TypeScript-only — non-TypeScript clients must implement validation separately","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.47,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.6,"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":"2026-06-17T09:51:04.689Z","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=web-search-mcp","compare_url":"https://unfragile.ai/compare?artifact=web-search-mcp"}},"signature":"jbRKOR0feeL+xgjRMSrsHJOUqrg1IoPOr7kRnc1yqazHXlN66a+T5Ehv/NR9HkWNewZAqQRbCiDDJ9MPJU6BAA==","signedAt":"2026-06-20T18:05:05.047Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/web-search-mcp","artifact":"https://unfragile.ai/web-search-mcp","verify":"https://unfragile.ai/api/v1/verify?slug=web-search-mcp","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"}}