{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-search1api","slug":"search1api","name":"Search1API","type":"mcp","url":"https://github.com/fatwang2/search1api-mcp","page_url":"https://unfragile.ai/search1api","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-search1api__cap_0","uri":"capability://search.retrieval.multi.engine.web.search.with.filtering.and.time.range.constraints","name":"multi-engine web search with filtering and time-range constraints","description":"Implements standardized web search across multiple search engines (Google, Bing, DuckDuckGo, etc.) through the Search1API backend, with support for site-specific filtering, time-range queries, and result ranking. The MCP server acts as a protocol adapter that translates client search requests into Search1API calls, handling parameter normalization and response marshaling back through the MCP interface.","intents":["I need to search the web from within my AI agent without making direct API calls","I want to constrain searches to specific domains or time periods programmatically","I need real-time web data integrated into my LLM reasoning pipeline"],"best_for":["AI agents and assistants built on Claude Desktop, Cursor, or LibreChat","developers building MCP-compatible applications that need web search capabilities","teams integrating real-time web data into LLM-powered workflows"],"limitations":["Depends entirely on Search1API backend availability and rate limits","Search result quality and coverage varies by underlying search engine","No local caching of search results — each query hits the remote API","Time-range filtering effectiveness depends on search engine support"],"requires":["Node.js 18.0.0 or higher","Valid Search1API key from Search1API service","MCP client compatible with tool calling (Claude Desktop, Cursor, LibreChat, etc.)"],"input_types":["query string","optional site filter (domain)","optional time range (e.g., 'last week', 'last month')"],"output_types":["structured JSON with search results","title, URL, snippet, and relevance metadata per result"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-search1api__cap_1","uri":"capability://search.retrieval.real.time.news.search.with.temporal.filtering","name":"real-time news search with temporal filtering","description":"Provides access to recent news articles from multiple sources through Search1API, with built-in time-range filtering to retrieve articles from specific periods (e.g., last 24 hours, last week). The MCP server wraps Search1API's news endpoint and normalizes responses into a consistent schema that includes publication date, source, headline, and summary, enabling time-aware news retrieval for AI agents.","intents":["I want my AI agent to fetch recent news articles on a topic without manual source aggregation","I need to constrain news results to a specific time window for temporal relevance","I want to integrate breaking news into my LLM's context for up-to-date reasoning"],"best_for":["news aggregation and summarization agents","financial or market analysis tools that need real-time data","research assistants that require current event context"],"limitations":["News availability depends on Search1API's source coverage and indexing latency","No filtering by news category, sentiment, or source reliability built into the MCP tool","Time-range filtering is coarse-grained (days/weeks, not minute-level precision)","Results are read-only snapshots — no subscription or alert functionality"],"requires":["Node.js 18.0.0 or higher","Valid Search1API key with news search access","MCP client with tool calling support"],"input_types":["query string (topic or keyword)","optional time range parameter"],"output_types":["structured JSON array of news articles","fields: headline, source, publication_date, summary, URL"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-search1api__cap_10","uri":"capability://automation.workflow.error.handling.and.response.normalization.across.search1api.endpoints","name":"error handling and response normalization across search1api endpoints","description":"Implements centralized error handling that catches failures from Search1API (network errors, rate limits, invalid responses) and translates them into standardized MCP error responses with descriptive messages. The server normalizes responses from different Search1API endpoints into consistent JSON structures, handling variations in response format and ensuring clients receive predictable output regardless of which tool is invoked.","intents":["I want clear error messages when Search1API calls fail instead of raw API errors","I need consistent response formats across all MCP tools for easier client-side handling","I want my agent to gracefully handle API failures and rate limiting"],"best_for":["production MCP deployments that need robust error handling","applications requiring consistent response schemas","systems that need to distinguish between client errors and server failures"],"limitations":["Error messages are generic — no detailed error codes from Search1API are exposed","Rate limit handling is passive (returns error) — no automatic retry or backoff","Response normalization may lose Search1API-specific metadata","No logging or monitoring integration — errors are only visible to clients"],"requires":["Node.js 18.0.0 or higher"],"input_types":["raw Search1API responses (success or error)"],"output_types":["normalized JSON response or MCP error object"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-search1api__cap_2","uri":"capability://data.processing.analysis.full.page.content.extraction.and.html.to.text.conversion","name":"full-page content extraction and html-to-text conversion","description":"Extracts complete readable content from web pages by sending URLs to Search1API's crawl endpoint, which performs server-side HTML parsing, boilerplate removal, and text extraction. The MCP server receives the cleaned content and returns it as structured text, enabling AI agents to analyze webpage content without implementing their own HTML parsing or managing browser automation.","intents":["I need to extract the main content from a webpage for my AI agent to analyze","I want to convert HTML pages to clean text without boilerplate, ads, or navigation","I need to feed webpage content into my LLM's context window efficiently"],"best_for":["content analysis and summarization agents","research tools that aggregate information from multiple web sources","document extraction pipelines that need to process web pages"],"limitations":["Relies on Search1API's crawling infrastructure — cannot crawl pages blocked by robots.txt or requiring authentication","JavaScript-rendered content may not be fully extracted if Search1API doesn't execute JS","Large pages may be truncated or summarized by the backend","No support for extracting structured data (tables, lists) in their original format — returns plain text only","Extraction quality varies by page structure and complexity"],"requires":["Node.js 18.0.0 or higher","Valid Search1API key with crawl access","MCP client with tool calling support","Valid, publicly accessible URL"],"input_types":["URL string (http/https)"],"output_types":["plain text content","optional metadata (title, description)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-search1api__cap_3","uri":"capability://search.retrieval.website.sitemap.generation.and.link.extraction","name":"website sitemap generation and link extraction","description":"Generates a sitemap of related links from a given website by querying Search1API's sitemap endpoint, which crawls the site and extracts internal link structure. The MCP server returns a structured list of discovered URLs organized by relevance or hierarchy, enabling agents to understand site structure and discover related content without manual link following.","intents":["I want to discover all pages on a website programmatically for bulk analysis","I need to understand the structure and navigation of a site before crawling specific pages","I want to find related content on a domain without manually following links"],"best_for":["site analysis and SEO tools","content discovery agents that need to map domain structure","bulk content extraction pipelines"],"limitations":["Sitemap generation is limited to publicly crawlable pages — respects robots.txt","Large sites may return truncated results or only top-level links","No support for dynamic or JavaScript-generated links","Results depend on site structure clarity — poorly linked sites may yield incomplete maps","No filtering by content type or page category"],"requires":["Node.js 18.0.0 or higher","Valid Search1API key with sitemap access","MCP client with tool calling support","Valid domain URL"],"input_types":["domain or root URL"],"output_types":["structured JSON array of discovered URLs","optional metadata per URL (title, depth, relevance)"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-search1api__cap_4","uri":"capability://planning.reasoning.complex.reasoning.with.deepseek.r1.model.integration","name":"complex reasoning with deepseek r1 model integration","description":"Exposes DeepSeek R1's chain-of-thought reasoning capabilities as an MCP tool, allowing AI agents to offload complex problem-solving tasks to a specialized reasoning model. The server sends reasoning prompts to Search1API's reasoning endpoint, which invokes DeepSeek R1 and returns structured reasoning chains along with final answers, enabling multi-step logical inference without implementing reasoning logic in the client.","intents":["I want my AI agent to perform deep reasoning on complex problems without implementing chain-of-thought myself","I need to decompose a problem into reasoning steps and get structured explanations","I want to leverage DeepSeek R1's reasoning capabilities within my MCP-based application"],"best_for":["research and analysis agents that need rigorous reasoning","educational tools that require step-by-step explanations","complex problem-solving systems that benefit from explicit reasoning chains"],"limitations":["Reasoning latency is higher than direct LLM calls due to chain-of-thought expansion","DeepSeek R1 availability and rate limits depend on Search1API's backend","No control over reasoning depth or token budget — server-side defaults apply","Reasoning output is text-based; no structured extraction of reasoning steps","Cost per reasoning query may be higher than standard LLM calls"],"requires":["Node.js 18.0.0 or higher","Valid Search1API key with reasoning access","MCP client with tool calling support"],"input_types":["problem statement or question (text)"],"output_types":["reasoning chain (text with step-by-step logic)","final answer or conclusion"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-search1api__cap_5","uri":"capability://search.retrieval.trending.topics.aggregation.from.github.and.hacker.news","name":"trending topics aggregation from github and hacker news","description":"Aggregates trending topics and discussions from GitHub and Hacker News through Search1API, providing agents with real-time insights into developer community trends and popular discussions. The MCP server queries Search1API's trending endpoint and returns a ranked list of trending items with metadata (title, discussion count, upvotes, source), enabling agents to stay informed about emerging topics without polling multiple sources.","intents":["I want my agent to know what's trending in the developer community right now","I need to fetch popular discussions from Hacker News and GitHub for context","I want to integrate trending topics into my agent's awareness without manual source polling"],"best_for":["developer news and trend analysis tools","community-aware AI assistants","research agents tracking emerging technologies"],"limitations":["Trending data is aggregated by Search1API — no direct control over ranking algorithm","Results are snapshots at query time; no historical trending data or time-series analysis","Limited to GitHub and Hacker News; no other community platforms","Trending items may be outdated if Search1API's cache is stale","No filtering by topic, language, or category"],"requires":["Node.js 18.0.0 or higher","Valid Search1API key with trending access","MCP client with tool calling support"],"input_types":["optional source filter (github, hackernews, or both)"],"output_types":["structured JSON array of trending items","fields: title, source, discussion_count, upvotes, URL"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-search1api__cap_6","uri":"capability://tool.use.integration.mcp.protocol.server.with.stdio.transport.and.tool.registry","name":"mcp protocol server with stdio transport and tool registry","description":"Implements a full Model Context Protocol server using Node.js that exposes all Search1API capabilities as standardized MCP tools. The server manages STDIO-based communication with MCP clients, maintains a tool registry with JSON schema definitions for each tool, handles request routing and response marshaling, and manages the lifecycle of tool invocations. Built on the MCP SDK, it translates between MCP's tool calling convention and Search1API's HTTP API.","intents":["I want to integrate Search1API capabilities into my MCP-compatible application (Claude Desktop, Cursor, LibreChat)","I need a standardized tool interface for web search, crawling, and reasoning without custom API integration","I want to deploy a local MCP server that bridges my AI client to Search1API"],"best_for":["developers building MCP-compatible AI applications","teams deploying Claude Desktop or Cursor with web capabilities","LibreChat users wanting to add Search1API integration"],"limitations":["STDIO transport is single-threaded — concurrent requests may queue","No built-in request caching or result deduplication across clients","Tool schema is static — no dynamic tool registration at runtime","Error handling relies on MCP error protocol — client-side error recovery depends on client implementation","No authentication between MCP client and server — assumes trusted local environment"],"requires":["Node.js 18.0.0 or higher","Valid Search1API key (passed via environment variable or configuration)","MCP client compatible with STDIO transport (Claude Desktop, Cursor, LibreChat, etc.)"],"input_types":["MCP tool call requests (JSON-RPC format)"],"output_types":["MCP tool call responses (JSON-RPC format)","structured results from underlying Search1API tools"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-search1api__cap_7","uri":"capability://automation.workflow.api.key.management.with.environment.variable.and.configuration.file.support","name":"api key management with environment variable and configuration file support","description":"Manages Search1API credentials through multiple configuration methods: environment variables (SEARCH1API_KEY), .env files for local development, and configuration files for Docker deployments. The server reads credentials at startup and uses them for all subsequent Search1API calls, supporting both standalone and containerized deployments without requiring clients to manage credentials directly.","intents":["I want to configure my MCP server with Search1API credentials securely without hardcoding them","I need to support multiple deployment scenarios (local, Docker, production) with different credential sources","I want my MCP server to work with LibreChat's Docker setup without manual credential passing"],"best_for":["developers deploying MCP servers in multiple environments","teams using Docker and LibreChat integration","security-conscious deployments that separate credentials from code"],"limitations":["Credentials are loaded at server startup — no runtime credential rotation","Environment variable approach exposes credentials in process listings (mitigated by .env files)",".env file support requires file system access — not suitable for serverless environments","No credential validation at startup — invalid keys only fail when first API call is made","Single credential per server — no multi-tenant or per-client credential isolation"],"requires":["Node.js 18.0.0 or higher","Valid Search1API key from Search1API service","Either SEARCH1API_KEY environment variable or .env file in working directory"],"input_types":["environment variable or .env file"],"output_types":["validated API key for internal use (not exposed to clients)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-search1api__cap_8","uri":"capability://automation.workflow.librechat.docker.integration.with.volume.based.configuration","name":"librechat docker integration with volume-based configuration","description":"Provides Docker Compose configuration and setup instructions for deploying the MCP server alongside LibreChat, with support for credential injection via Docker volumes and environment variables. The server can be containerized and linked to LibreChat's container network, allowing LibreChat to invoke Search1API tools through the MCP server without requiring separate API key management in LibreChat itself.","intents":["I want to add Search1API capabilities to my LibreChat deployment via Docker","I need to configure the MCP server to work with LibreChat's tool calling system","I want to manage Search1API credentials in a single place for my entire LibreChat setup"],"best_for":["LibreChat users wanting to add web search and crawling capabilities","teams deploying LibreChat in Docker environments","self-hosted AI chat applications that need web integration"],"limitations":["Requires Docker and Docker Compose — not suitable for non-containerized deployments","Volume-based configuration requires file system access on the host","Network communication between containers adds latency compared to local execution","Debugging container-to-container communication can be complex","LibreChat integration requires specific MCP client support in LibreChat version"],"requires":["Docker and Docker Compose installed","LibreChat container running on the same Docker network","Valid Search1API key","Docker volume or environment variable for credential passing"],"input_types":["Docker Compose configuration","environment variables or mounted .env file"],"output_types":["running MCP server container accessible to LibreChat"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-search1api__cap_9","uri":"capability://code.generation.editing.typescript.type.definitions.and.schema.validation.for.tool.parameters","name":"typescript type definitions and schema validation for tool parameters","description":"Defines TypeScript interfaces and JSON schemas for all tool parameters and responses, enabling compile-time type checking and runtime validation of tool invocations. The server validates incoming MCP requests against these schemas before forwarding to Search1API, catching malformed requests early and providing clear error messages. Schemas are also exposed to MCP clients for tool discovery and parameter hints.","intents":["I want type-safe tool invocations with compile-time checking in my MCP client","I need to validate tool parameters before sending them to Search1API","I want my IDE to provide autocomplete and parameter hints for MCP tools"],"best_for":["TypeScript-based MCP clients and applications","development teams using strict type checking","applications requiring parameter validation before API calls"],"limitations":["Type definitions are TypeScript-only — Python or other language clients must manually implement validation","Schema validation adds ~10-50ms overhead per tool invocation","Complex nested types may not translate perfectly to JSON schema","Type definitions must be manually kept in sync with Search1API API changes"],"requires":["TypeScript 4.5+ for type checking","MCP client with schema support for parameter hints"],"input_types":["tool parameter objects (TypeScript interfaces)"],"output_types":["validated parameters ready for API call","JSON schema for MCP tool discovery"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":27,"verified":false,"data_access_risk":"high","permissions":["Node.js 18.0.0 or higher","Valid Search1API key from Search1API service","MCP client compatible with tool calling (Claude Desktop, Cursor, LibreChat, etc.)","Valid Search1API key with news search access","MCP client with tool calling support","Valid Search1API key with crawl access","Valid, publicly accessible URL","Valid Search1API key with sitemap access","Valid domain URL","Valid Search1API key with reasoning access"],"failure_modes":["Depends entirely on Search1API backend availability and rate limits","Search result quality and coverage varies by underlying search engine","No local caching of search results — each query hits the remote API","Time-range filtering effectiveness depends on search engine support","News availability depends on Search1API's source coverage and indexing latency","No filtering by news category, sentiment, or source reliability built into the MCP tool","Time-range filtering is coarse-grained (days/weeks, not minute-level precision)","Results are read-only snapshots — no subscription or alert functionality","Error messages are generic — no detailed error codes from Search1API are exposed","Rate limit handling is passive (returns error) — no automatic retry or backoff","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.32,"ecosystem":0.39999999999999997,"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":"2026-06-17T09:51:04.049Z","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=search1api","compare_url":"https://unfragile.ai/compare?artifact=search1api"}},"signature":"3ocz4w3kDDAYa0exoAI8BjJxCcFOLlZ9Ho0Bjr/UcuZ1JONnSHz9VZSi9SvitW86yTJgWo+PUR4IQBn50LaXDQ==","signedAt":"2026-06-21T00:50:25.241Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/search1api","artifact":"https://unfragile.ai/search1api","verify":"https://unfragile.ai/api/v1/verify?slug=search1api","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"}}