{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github_mcp-sansan0-trendradar","slug":"mcp-sansan0-trendradar","name":"TrendRadar","type":"repo","url":"https://github.com/sansan0/TrendRadar","page_url":"https://unfragile.ai/mcp-sansan0-trendradar","categories":["automation","observability"],"tags":["ai","bark","data-analysis","docker","hot-news","llm","mail","mcp","mcp-server","news","ntfy","python","rss","trending-topics","wechat","wework"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github_mcp-sansan0-trendradar__cap_0","uri":"capability://data.processing.analysis.multi.platform.trending.topic.aggregation.with.unified.normalization","name":"multi-platform trending topic aggregation with unified normalization","description":"Crawls 11+ heterogeneous platforms (Zhihu, Weibo, Bilibili, Twitter, Reddit, HackerNews, etc.) and RSS feeds using platform-specific scrapers, normalizes disparate data schemas into a unified NewsItem model, and deduplicates content across sources using fuzzy title matching and URL canonicalization. The system maintains platform-specific metadata (rank, heat value, engagement metrics) while presenting a single normalized feed, enabling cross-platform trend detection that would be invisible within individual platform silos.","intents":["Monitor trending topics across Chinese and international platforms without manually checking each app","Detect emerging trends that spike simultaneously across multiple platforms","Build a unified news feed that aggregates RSS subscriptions with social media hotspots","Track the same story as it propagates across different platforms with different narratives"],"best_for":["Investors and traders monitoring market-moving news across multiple sources","Content creators identifying trending topics for video/article ideas","PR professionals tracking brand mentions and sentiment across platforms","News aggregation services building multi-source feeds"],"limitations":["Platform-specific scrapers are brittle to UI/API changes; requires maintenance when platforms update","Rate limiting on source platforms may cause incomplete data collection during peak traffic","Deduplication is fuzzy-match based, not semantic; similar but distinct stories may be merged","Chinese platform support is stronger than international; some Western platforms have limited coverage"],"requires":["Python 3.9+","Network access to 11+ platforms (may require VPN for some Chinese platforms)","Platform-specific headers/cookies for authenticated scraping (optional but recommended)","Storage backend (local filesystem, S3, or cloud storage for caching)"],"input_types":["Platform URLs (Zhihu, Weibo, Bilibili, etc.)","RSS feed URLs","Platform API endpoints"],"output_types":["Normalized NewsItem objects (title, content, source, timestamp, platform metadata)","Deduplicated news feed (JSON/CSV)","Platform-specific metadata (rank, heat, engagement)"],"categories":["data-processing-analysis","search-retrieval","web-scraping"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-sansan0-trendradar__cap_1","uri":"capability://data.processing.analysis.keyword.based.content.filtering.with.regex.and.boolean.logic","name":"keyword-based content filtering with regex and boolean logic","description":"Implements a multi-stage filtering pipeline that matches news items against user-defined keywords using regex patterns, required word lists, and excluded word lists. The system applies frequency-based scoring (keyword occurrence count) combined with platform hotspot weights to rank filtered results. Configuration is stored in frequency_words.txt with support for regex patterns, AND/OR/NOT boolean operators, and per-keyword weighting. Filtering occurs at collection time (reducing storage) and again at report generation time (enabling dynamic reconfiguration without re-crawling).","intents":["Filter news to only topics I care about (e.g., 'AI', 'BYD', 'Education Policy') without manual review","Create multiple keyword profiles for different interests (investor keywords vs. content creator keywords)","Exclude noise keywords (e.g., exclude 'celebrity gossip' from tech news)","Use regex patterns to match variations (e.g., 'AI|artificial intelligence|machine learning')"],"best_for":["Users with specific investment theses or content niches","Teams managing multiple keyword profiles for different departments","Developers building custom filtering logic on top of TrendRadar","Organizations with strict content policies requiring keyword-based moderation"],"limitations":["Regex-based matching is shallow; cannot understand semantic similarity (e.g., 'LLM' and 'large language model' require explicit regex)","Boolean logic is limited to AND/OR/NOT; no complex nested expressions","Keyword matching is case-sensitive by default; requires regex flags for case-insensitive matching","No semantic understanding; homonyms and polysemy cause false positives (e.g., 'Apple' matches both fruit and company)"],"requires":["frequency_words.txt configuration file with keyword definitions","Python regex syntax knowledge for complex patterns","Understanding of boolean logic (AND/OR/NOT operators)"],"input_types":["Keyword strings (plain text or regex patterns)","Boolean operators (AND, OR, NOT)","Weighting values (numeric multipliers)"],"output_types":["Filtered news items (matching keywords)","Relevance scores (keyword frequency × weight × platform hotspot)","Filter statistics (match count, coverage percentage)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-sansan0-trendradar__cap_10","uri":"capability://data.processing.analysis.real.time.new.topic.detection.with.markers.and.trend.emergence.tracking","name":"real-time new topic detection with 🆕 markers and trend emergence tracking","description":"Detects newly emerged topics by comparing current crawl results against historical data stored in the system. Topics are marked as 🆕 (new) if they appear for the first time in the current crawl or if their hotspot rank increased significantly compared to previous crawls. The system tracks topic emergence velocity (how quickly a topic rises in rankings) and flags topics with unusual acceleration. New topic detection is performed at report generation time, enabling dynamic detection without re-crawling. The system maintains a historical hotspot index for comparison.","intents":["Identify breaking news and emerging trends in real-time","Distinguish between sustained trends and temporary spikes","Track how quickly topics gain traction across platforms","Get alerts when topics suddenly become hot"],"best_for":["News organizations tracking breaking stories","Traders identifying market-moving news early","Content creators spotting emerging trends before they peak","Investors monitoring emerging risks and opportunities"],"limitations":["New topic detection requires historical data; first crawl has no baseline for comparison","Emergence velocity is calculated from discrete crawls; continuous monitoring would be more accurate","Topics that were previously hot but disappeared may be marked as 🆕 if they re-emerge","Threshold for 'significant increase' is fixed; may not adapt to different market conditions","No semantic understanding; similar topics with different names are treated as separate"],"requires":["Historical hotspot data from previous crawls","Current crawl results with hotspot rankings","Comparison threshold configuration (e.g., 50% rank increase = new topic)"],"input_types":["Current hotspot rankings (from crawl)","Historical hotspot rankings (from previous crawls)","Topic metadata (title, platform, rank)"],"output_types":["New topic markers (🆕 flag)","Emergence velocity (rank change per time unit)","New topic statistics (count, average velocity)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-sansan0-trendradar__cap_11","uri":"capability://tool.use.integration.visual.configuration.editor.with.real.time.validation.and.preview","name":"visual configuration editor with real-time validation and preview","description":"Provides a web-based UI for editing TrendRadar configuration files (config.yaml, frequency_words.txt, timeline.yaml) with real-time validation and preview. The editor supports: (1) syntax highlighting for YAML and regex, (2) validation of keyword patterns (regex compilation check), (3) preview of filtered results based on current keyword configuration, (4) drag-and-drop channel configuration, (5) schedule preview (shows next 10 execution times). Changes are validated before saving, preventing configuration errors. The editor is optional; users can edit config files directly.","intents":["Configure TrendRadar without editing YAML files manually","Validate keyword regex patterns before deploying","Preview how keywords will filter news before running crawl","Visualize scheduler timeline to verify execution schedule"],"best_for":["Non-technical users configuring TrendRadar","Teams wanting to validate configuration before deployment","Users iterating on keyword filters and wanting quick feedback","Organizations with strict configuration management policies"],"limitations":["Web UI adds operational complexity; requires separate web server process","Preview is based on sample data; actual crawl results may differ","Regex validation is basic; complex patterns may pass validation but fail at runtime","No authentication; UI should be deployed behind auth layer in production","Changes are not persisted until explicitly saved; accidental navigation loses edits"],"requires":["Web server running (Flask, FastAPI, etc.)","Network access to web UI (localhost or internal network)","TrendRadar configuration files accessible to web server"],"input_types":["Configuration file content (YAML, text)","Sample news data (for preview)","Validation rules (regex syntax, YAML structure)"],"output_types":["Validated configuration files","Preview results (filtered news items)","Validation errors (with line numbers and suggestions)","Schedule preview (next execution times)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-sansan0-trendradar__cap_2","uri":"capability://text.generation.language.ai.powered.news.analysis.and.summarization.via.litellm.multi.provider.abstraction","name":"ai-powered news analysis and summarization via litellm multi-provider abstraction","description":"Integrates LiteLLM to provide vendor-agnostic AI analysis and summarization of filtered news items. Users configure their preferred LLM provider (OpenAI, Anthropic, Ollama, local models, etc.) once in config.yaml, and the system automatically routes analysis requests to that provider. The AI analysis capability includes: (1) automated summarization of long articles into key points, (2) sentiment analysis (positive/negative/neutral), (3) trend prediction based on historical patterns, and (4) custom analysis prompts. Analysis results are cached to avoid redundant API calls and can be pushed directly to notification channels.","intents":["Get AI-generated summaries of trending news without reading full articles","Understand sentiment and implications of news (bullish/bearish for investments)","Predict future trends based on current news patterns and historical data","Run custom analysis prompts on news items (e.g., 'extract regulatory changes')"],"best_for":["Investors who need rapid analysis of market-moving news","Content creators generating commentary on trending topics","Teams with heterogeneous LLM preferences (some use OpenAI, others prefer local models)","Organizations with cost constraints (can switch to cheaper providers via LiteLLM)"],"limitations":["LiteLLM abstraction adds ~100-200ms latency per analysis request due to provider routing","API costs scale with news volume; high-frequency crawling can incur significant LLM charges","Analysis quality depends on underlying model; cheaper models (e.g., GPT-3.5) produce lower-quality summaries","No built-in fact-checking; AI summaries may hallucinate or misrepresent source content","Requires valid API credentials for chosen provider; local models require sufficient GPU memory"],"requires":["LiteLLM library (installed via pip)","API key for chosen provider (OpenAI, Anthropic, etc.) OR local model setup (Ollama, vLLM)","Network access to provider API OR local GPU for running models","config.yaml with provider configuration (model name, API key, temperature, max_tokens)"],"input_types":["News item text (title + content)","Custom analysis prompt (optional)","Historical news data (for trend prediction)"],"output_types":["Summarized text (key points, 1-3 sentences)","Sentiment label (positive/negative/neutral)","Trend prediction (emerging/declining/stable)","Custom analysis output (varies by prompt)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-sansan0-trendradar__cap_3","uri":"capability://text.generation.language.ai.powered.translation.with.multi.language.support.and.caching","name":"ai-powered translation with multi-language support and caching","description":"Leverages LiteLLM to translate news content from source languages (primarily Chinese) to target languages (English, etc.) on-demand. The system detects source language automatically (via langdetect or similar), caches translations to avoid re-translating identical content, and batches translation requests to reduce API calls. Translations are stored alongside original content, enabling bilingual reports and multi-language notification delivery. Translation can be triggered at collection time (all news) or report time (only filtered news).","intents":["Read Chinese trending news in English without manual translation","Build multilingual news feeds for international audiences","Translate news summaries for non-native speakers","Reduce translation API costs by caching and batching requests"],"best_for":["International teams monitoring Chinese market trends","Multilingual content creators needing news in multiple languages","Organizations serving non-English-speaking users","Cost-conscious teams using local translation models"],"limitations":["Translation quality varies by provider and model; cheaper models produce lower-quality translations","Language detection is probabilistic; mixed-language content may be misclassified","Caching is content-based; minor content variations bypass cache and trigger new translations","Batch translation adds latency (waits for batch to fill before sending to API)","Some specialized terminology (e.g., Chinese financial terms) may be mistranslated"],"requires":["LiteLLM library with translation-capable provider (OpenAI, Anthropic, etc.)","API credentials for translation provider","langdetect or similar library for language detection","Storage backend for caching translations"],"input_types":["News text in source language (primarily Chinese)","Target language code (e.g., 'en', 'es', 'fr')","Translation prompt (optional custom instructions)"],"output_types":["Translated text in target language","Language detection result (source language)","Translation metadata (provider, timestamp, cost)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-sansan0-trendradar__cap_4","uri":"capability://tool.use.integration.multi.channel.notification.delivery.with.atomic.message.batching","name":"multi-channel notification delivery with atomic message batching","description":"Implements a notification abstraction layer supporting 9+ delivery channels (WeChat, WeWork, Feishu, Telegram, Email, ntfy, Bark, Slack, etc.). Each channel has a provider-specific formatter that converts normalized news items into channel-appropriate messages (e.g., WeChat card format, Telegram markdown, email HTML). The system batches notifications atomically—all news items for a report are sent as a single batch to each channel, ensuring consistency and reducing API calls. Message formatting respects channel constraints (character limits, attachment limits, etc.) and supports templating for customization.","intents":["Receive trending news alerts on my preferred messaging platform (WeChat, Telegram, etc.)","Get news delivered to multiple channels simultaneously (e.g., Slack for work, WeChat for personal)","Customize notification format per channel (e.g., detailed summaries in email, brief alerts in Telegram)","Batch notifications to avoid spam (e.g., one daily digest instead of 100 individual alerts)"],"best_for":["Teams using diverse communication platforms (WeChat, Slack, Telegram, etc.)","Users wanting news on mobile (Bark, ntfy) and desktop (Slack, email) simultaneously","Organizations with strict notification policies (batching, rate limiting)","Developers building custom notification channels"],"limitations":["Each channel requires separate authentication (API key, webhook URL, etc.); setup is tedious for many channels","Message formatting is channel-specific; complex formatting may not translate across channels","Character/attachment limits vary by channel; long summaries may be truncated","Delivery is not guaranteed; some channels (ntfy, Bark) have no delivery confirmation","Rate limiting per channel may cause delivery delays during high-volume periods"],"requires":["API credentials for each enabled notification channel (WeChat token, Telegram bot token, etc.)","config.yaml with channel configuration (enabled channels, credentials, formatting options)","Network access to notification provider APIs","For email: SMTP server configuration"],"input_types":["Normalized news items (title, content, source, metadata)","Channel-specific configuration (API key, webhook URL, formatting template)","Report metadata (timestamp, filter name, item count)"],"output_types":["Channel-specific formatted messages (WeChat card, Telegram markdown, email HTML, etc.)","Delivery status (success/failure per channel)","Delivery logs (timestamp, channel, message ID)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-sansan0-trendradar__cap_5","uri":"capability://tool.use.integration.model.context.protocol.mcp.server.for.conversational.trend.analysis","name":"model context protocol (mcp) server for conversational trend analysis","description":"Exposes TrendRadar's data and analysis capabilities as an MCP server, enabling AI agents and LLM applications to query trends, perform analysis, and generate insights through natural language. The MCP server implements tools for: (1) querying filtered news by keyword/date/platform, (2) retrieving trend statistics and hotspot rankings, (3) running custom analysis on news subsets, (4) generating reports in various formats. Clients (Claude, other LLM agents) can invoke these tools via MCP protocol, enabling conversational exploration of trends without direct database access. The server maintains state across multiple requests, allowing multi-turn conversations about trends.","intents":["Ask Claude 'What are the top AI trends this week?' and get analyzed results","Build AI agents that autonomously monitor trends and trigger actions","Enable non-technical users to explore trends via natural language queries","Integrate TrendRadar data into larger AI agent workflows"],"best_for":["AI agent developers building trend-aware applications","Teams using Claude or other MCP-compatible LLMs for analysis","Organizations wanting conversational interfaces to trend data","Developers building custom AI applications on top of TrendRadar"],"limitations":["MCP server adds operational complexity; requires separate process management and monitoring","LLM agents may misinterpret tool outputs or generate incorrect queries","No built-in authentication; MCP server should be deployed behind auth layer in production","Tool invocation latency depends on underlying data queries; large date ranges may timeout","Conversational context is stateless per request; multi-turn conversations require explicit context passing"],"requires":["MCP server running (separate Python process or Docker container)","MCP-compatible client (Claude, custom agent framework)","Network connectivity between client and MCP server","TrendRadar data backend (local storage or cloud storage)"],"input_types":["Natural language queries (via LLM client)","Tool parameters (keyword, date range, platform filter, analysis type)","Conversation context (previous queries and results)"],"output_types":["Structured tool results (news items, statistics, analysis)","Natural language responses (generated by LLM client)","Report formats (JSON, markdown, HTML)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-sansan0-trendradar__cap_6","uri":"capability://data.processing.analysis.flexible.report.generation.with.multiple.output.modes.and.formats","name":"flexible report generation with multiple output modes and formats","description":"Generates customizable trend reports in multiple formats (JSON, CSV, Markdown, HTML, plain text) with configurable content sections (summary, hotspots, new topics, platform breakdown, sentiment analysis, etc.). The system supports three report modes: (1) SIMPLE mode (top N items with basic metadata), (2) DETAILED mode (full content + analysis + sentiment), (3) ANALYSIS mode (AI-generated insights + trend predictions). Reports can be filtered by keyword, date range, platform, and hotspot threshold. Report generation is decoupled from notification delivery, enabling on-demand report generation and archival.","intents":["Generate daily/weekly trend reports for stakeholders","Export filtered news to CSV for analysis in Excel/BI tools","Create HTML reports for sharing via email or web","Archive trend data in JSON for historical analysis"],"best_for":["Teams generating regular trend reports for leadership","Data analysts exporting news for further analysis","Content creators building reports for audiences","Organizations archiving trend data for compliance"],"limitations":["Report generation is synchronous; large reports (1000+ items) may take minutes to generate","HTML reports are basic; no interactive charts or visualizations","CSV export flattens nested data (analysis, metadata); some information is lost","Report templates are fixed; custom layouts require code changes","No pagination for large reports; all data is included in single file"],"requires":["Filtered news data (from keyword filtering stage)","Report configuration (mode, format, sections, filters)","Optional: AI analysis results (for ANALYSIS mode)"],"input_types":["Filtered news items","Report parameters (mode, format, date range, filters)","Template configuration (sections, styling)"],"output_types":["Report files (JSON, CSV, Markdown, HTML, plain text)","Report statistics (item count, platform breakdown, sentiment distribution)","Report metadata (generation timestamp, filter applied, data source)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-sansan0-trendradar__cap_7","uri":"capability://automation.workflow.scheduled.execution.with.timeline.based.workflow.orchestration","name":"scheduled execution with timeline-based workflow orchestration","description":"Implements a scheduler that executes the entire TrendRadar pipeline (crawl → filter → analyze → report → notify) on a configurable schedule defined in timeline.yaml. The scheduler supports multiple execution modes: (1) fixed intervals (e.g., every 6 hours), (2) cron expressions (e.g., '0 9 * * MON-FRI'), (3) one-time execution. Each scheduled job can have different configurations (keywords, report mode, notification channels), enabling multiple independent workflows. The scheduler handles job queuing, error recovery, and execution logging. Execution can be triggered via GitHub Actions (cloud), Docker (containerized), or local Python process.","intents":["Automatically crawl and analyze trends on a daily/weekly schedule","Run different workflows at different times (e.g., market-focused crawl during trading hours)","Recover from failures automatically (retry logic, error notifications)","Monitor scheduler health and execution logs"],"best_for":["Teams wanting fully automated trend monitoring without manual intervention","Organizations with multiple workflows (different keywords, schedules, channels)","Developers deploying TrendRadar in production environments","Users wanting scheduled reports (daily digest, weekly summary)"],"limitations":["Scheduler is single-threaded; concurrent jobs are queued and executed sequentially","Cron expressions are limited to standard syntax; complex schedules require multiple jobs","Execution timing is not guaranteed; system load may cause delays","No built-in job history or audit trail; requires external logging for compliance","Timezone handling is implicit; all times are in server timezone"],"requires":["timeline.yaml configuration with job definitions (schedule, keywords, report mode, channels)","Scheduler process running continuously (GitHub Actions, Docker, or local Python)","All dependencies for crawling, analysis, and notification (API keys, storage, etc.)"],"input_types":["Schedule definition (interval, cron expression, or one-time)","Job configuration (keywords, report mode, notification channels)","Execution context (environment variables, secrets)"],"output_types":["Execution logs (timestamp, job name, status, duration)","Generated reports (in configured format)","Notifications (sent to configured channels)","Error notifications (if job fails)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-sansan0-trendradar__cap_8","uri":"capability://automation.workflow.multi.deployment.architecture.with.github.actions.docker.and.local.python","name":"multi-deployment architecture with github actions, docker, and local python","description":"Supports three distinct deployment models with identical functionality but different operational characteristics: (1) GitHub Actions deployment (serverless, cloud storage, no infrastructure management), (2) Docker deployment (containerized, local storage, self-hosted), (3) Local Python deployment (direct execution, minimal setup). Each deployment model uses the same core TrendRadar code but with environment-specific configuration (storage backend, scheduler implementation, notification credentials). The system abstracts storage (local filesystem, S3, cloud storage) and scheduler (GitHub Actions cron, Docker cron, APScheduler) behind interfaces, enabling seamless switching between deployment models.","intents":["Deploy TrendRadar without managing servers (GitHub Actions)","Run TrendRadar in containerized environment with Docker Compose","Run TrendRadar locally for development and testing","Migrate between deployment models without code changes"],"best_for":["Teams wanting serverless deployment (GitHub Actions)","Organizations with Docker infrastructure (Kubernetes, Docker Swarm)","Developers testing locally before production deployment","Teams wanting flexibility to switch deployment models"],"limitations":["GitHub Actions has 6-hour job timeout; long-running crawls may be interrupted","GitHub Actions free tier has limited monthly minutes; high-frequency jobs require paid plan","Docker deployment requires container orchestration knowledge; not suitable for non-technical users","Local Python deployment requires manual process management; no automatic restart on failure","Storage abstraction adds complexity; debugging storage issues requires understanding multiple backends"],"requires":["For GitHub Actions: GitHub repository with Actions enabled, cloud storage credentials (S3, etc.)","For Docker: Docker and Docker Compose installed, local storage or cloud storage credentials","For Local Python: Python 3.9+, all dependencies installed, process manager (systemd, supervisor, etc.)"],"input_types":["Deployment model selection (GitHub Actions / Docker / Local)","Environment-specific configuration (storage backend, scheduler type, credentials)","TrendRadar configuration files (config.yaml, timeline.yaml, frequency_words.txt)"],"output_types":["Deployed application (running scheduler, generating reports, sending notifications)","Execution logs (deployment-specific format)","Generated reports and notifications (same regardless of deployment model)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-sansan0-trendradar__cap_9","uri":"capability://data.processing.analysis.persistent.data.storage.with.local.and.cloud.backend.abstraction","name":"persistent data storage with local and cloud backend abstraction","description":"Implements a storage abstraction layer supporting multiple backends: local filesystem (for development/testing), AWS S3 (for cloud deployment), and other cloud storage services. The system stores: (1) raw crawled news items (for deduplication and historical analysis), (2) filtered reports (for archival and audit), (3) analysis results (for caching and reuse), (4) execution logs (for monitoring and debugging). Storage is organized by date and source for efficient querying. The abstraction enables switching backends via configuration without code changes. Data can be stored locally (for privacy) or in cloud (for accessibility and backup).","intents":["Archive trend data for historical analysis and compliance","Store crawled news for deduplication across multiple runs","Cache analysis results to avoid redundant LLM API calls","Access trend data from multiple machines (cloud storage)"],"best_for":["Organizations with data retention requirements (compliance, audit)","Teams wanting to analyze historical trends","Cost-conscious teams using local storage to avoid cloud costs","Distributed teams needing shared access to trend data"],"limitations":["Local storage is not shared; multiple instances cannot access same data","Cloud storage adds latency (~100-500ms per operation) compared to local storage","Cloud storage incurs costs (S3 storage, bandwidth); high-volume crawling can be expensive","Data organization by date assumes consistent date formats; timezone issues may cause misalignment","No built-in data encryption; sensitive data should be encrypted before storage"],"requires":["For local storage: writable filesystem with sufficient space (depends on crawl volume)","For cloud storage: cloud provider credentials (AWS S3 access key, etc.), cloud storage bucket"],"input_types":["News items (raw crawled data)","Reports (filtered and analyzed data)","Analysis results (LLM outputs)","Execution logs (scheduler and crawler logs)"],"output_types":["Stored data files (JSON, CSV, or raw format)","Data organization (by date, source, type)","Storage metadata (file size, creation date, access logs)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-sansan0-trendradar__headline","uri":"capability://automation.workflow.ai.driven.public.opinion.and.trend.monitoring.system","name":"ai-driven public opinion and trend monitoring system","description":"TrendRadar is an AI-powered tool that aggregates trending topics from multiple platforms, filters them by user-defined keywords, and delivers personalized notifications, helping users manage information overload effectively.","intents":["best AI trend monitoring tool","AI news aggregator for personalized alerts","top tools for monitoring public opinion","best software for trend analysis","AI-driven news filtering system"],"best_for":["investors","content creators","PR professionals"],"limitations":[],"requires":[],"input_types":["keywords","RSS feeds"],"output_types":["notifications","trend reports"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","Network access to 11+ platforms (may require VPN for some Chinese platforms)","Platform-specific headers/cookies for authenticated scraping (optional but recommended)","Storage backend (local filesystem, S3, or cloud storage for caching)","frequency_words.txt configuration file with keyword definitions","Python regex syntax knowledge for complex patterns","Understanding of boolean logic (AND/OR/NOT operators)","Historical hotspot data from previous crawls","Current crawl results with hotspot rankings","Comparison threshold configuration (e.g., 50% rank increase = new topic)"],"failure_modes":["Platform-specific scrapers are brittle to UI/API changes; requires maintenance when platforms update","Rate limiting on source platforms may cause incomplete data collection during peak traffic","Deduplication is fuzzy-match based, not semantic; similar but distinct stories may be merged","Chinese platform support is stronger than international; some Western platforms have limited coverage","Regex-based matching is shallow; cannot understand semantic similarity (e.g., 'LLM' and 'large language model' require explicit regex)","Boolean logic is limited to AND/OR/NOT; no complex nested expressions","Keyword matching is case-sensitive by default; requires regex flags for case-insensitive matching","No semantic understanding; homonyms and polysemy cause false positives (e.g., 'Apple' matches both fruit and company)","New topic detection requires historical data; first crawl has no baseline for comparison","Emergence velocity is calculated from discrete crawls; continuous monitoring would be more accurate","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.8863938899548487,"quality":0.49,"ecosystem":0.7000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"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":"2026-05-03T14:23:31.491Z","last_commit":"2026-04-30T02:09:11Z"},"community":{"stars":56210,"forks":24009,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mcp-sansan0-trendradar","compare_url":"https://unfragile.ai/compare?artifact=mcp-sansan0-trendradar"}},"signature":"sb9g0lQPtb2JAlLdW76+6wc3pRrM9tmnkpZxyfYzkGkngvCRHetShdSe8tEkTzQb6ryOipibq4c47BRHZPnKDQ==","signedAt":"2026-06-21T11:38:27.382Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-sansan0-trendradar","artifact":"https://unfragile.ai/mcp-sansan0-trendradar","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-sansan0-trendradar","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"}}