TrendRadar ranks higher at 58/100 vs @taazkareem/clickup-mcp-server at 45/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | @taazkareem/clickup-mcp-server | TrendRadar |
|---|---|---|
| Type | MCP Server | Repository |
| UnfragileRank | 45/100 | 58/100 |
| Adoption | 1 | 1 |
| Quality |
| 0 |
| 0 |
| Ecosystem | 1 | 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 11 decomposed | 12 decomposed |
| Times Matched | 0 | 0 |
Creates, updates, and deletes ClickUp tasks through MCP protocol handlers that translate natural language or structured requests into ClickUp API calls. Implements request validation, error handling, and response transformation to present task operations as native MCP tools callable by AI agents without direct API knowledge.
Unique: Exposes ClickUp task operations as native MCP tools rather than requiring agents to construct raw REST API calls, with built-in schema validation and error transformation specific to ClickUp's API response patterns
vs alternatives: Simpler than raw ClickUp API integration for LLM agents because MCP abstraction handles authentication, request formatting, and response parsing automatically
Searches and retrieves ClickUp documents from workspaces/spaces using MCP resource handlers that query the ClickUp API and return document metadata, content, and hierarchy. Implements pagination and filtering to handle large document collections without overwhelming agent context windows.
Unique: Implements MCP resource protocol for document retrieval, allowing agents to access ClickUp Docs as a knowledge source without manual API calls, with built-in pagination and metadata extraction
vs alternatives: More integrated than querying ClickUp API directly because MCP handles resource lifecycle and caching, reducing latency for repeated document access
Supports both personal API tokens and OAuth2 authentication flows for ClickUp, allowing secure credential management without exposing tokens in prompts. Implements token refresh logic and credential validation before making API calls.
Unique: Implements both OAuth2 and personal token authentication with automatic token refresh, allowing secure credential management without exposing secrets in agent prompts
vs alternatives: More secure than hardcoded tokens because OAuth enables credential rotation and user-level access control without storing secrets in configuration
Retrieves filtered task lists from ClickUp spaces/lists using MCP resource handlers that support multiple filter dimensions (status, assignee, priority, due date, custom fields). Implements efficient pagination and sorting to present task data to agents without requiring manual API query construction.
Unique: Exposes ClickUp's filter API as MCP resources with pre-built filter templates for common queries (by assignee, status, priority), reducing agent complexity vs raw API filter syntax
vs alternatives: Simpler than building custom filter logic because MCP abstracts ClickUp's filter query language and handles pagination automatically
Posts messages to ClickUp task comments and retrieves comment threads using MCP tool handlers that translate agent messages into ClickUp API calls. Supports rich text formatting, mentions, and attachment references while maintaining conversation context within task threads.
Unique: Integrates ClickUp task comments as an MCP tool, allowing agents to participate in task discussions and maintain audit trails within ClickUp's native interface rather than external logging systems
vs alternatives: More integrated than external logging because comments stay within ClickUp's task context, visible to all team members without context switching
Discovers and exposes ClickUp workspace structure (teams, spaces, lists, folders) through MCP resource handlers that query the ClickUp API and cache hierarchy metadata. Enables agents to understand available task containers and navigate the workspace without hardcoded IDs.
Unique: Exposes ClickUp workspace hierarchy as MCP resources with caching, allowing agents to dynamically discover task containers instead of requiring hardcoded space/list IDs in prompts
vs alternatives: More flexible than static configuration because agents can adapt to workspace changes without redeployment
Updates task metadata (status, priority, custom fields, due dates, assignees) through MCP tool handlers that validate field types and values against ClickUp's schema before submitting API calls. Implements field-type-aware transformations (date parsing, enum validation, number formatting) to prevent API errors.
Unique: Implements field-type-aware validation for ClickUp custom fields, preventing API errors by transforming agent-provided values to match ClickUp's schema before submission
vs alternatives: More robust than raw API calls because built-in validation catches type mismatches and enum violations before they reach ClickUp's API
Runs as a standalone MCP server process that exposes ClickUp capabilities via the Model Context Protocol, handling authentication, request routing, and response serialization. Supports multiple concurrent MCP clients (Claude Desktop, Cursor, Gemini CLI, n8n) through a single server instance with configurable logging and error handling.
Unique: Implements full MCP server specification with support for multiple transport types (stdio, SSE) and concurrent client connections, enabling seamless integration with Claude, Cursor, Gemini, and other MCP-compatible tools
vs alternatives: More flexible than direct API integration because MCP abstraction allows the same server to work with any MCP client without code changes
+3 more capabilities
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.
Unique: Implements platform-specific crawler modules with unified NewsItem schema and fuzzy deduplication across 11+ heterogeneous sources (Chinese + international), rather than relying on single-platform APIs or generic RSS parsing. Maintains platform-specific metadata (rank × 0.6 + frequency × 0.3 + platform hot value × 0.1) for weighted hotspot scoring.
vs alternatives: Covers more platforms (especially Chinese social media) with deeper metadata extraction than generic RSS aggregators, and provides unified deduplication across sources unlike single-platform monitoring tools.
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).
Unique: Combines regex pattern matching with frequency-based scoring and platform hotspot weighting (rank × 0.6 + frequency × 0.3 + platform hot value × 0.1) in a two-stage pipeline (collection-time and report-time filtering). Supports dynamic reconfiguration without re-crawling by applying filters at report generation.
TrendRadar scores higher at 58/100 vs @taazkareem/clickup-mcp-server at 45/100.
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vs alternatives: More flexible than simple keyword matching (supports regex and boolean logic) and more efficient than semantic filtering (no LLM overhead), making it suitable for real-time filtering at scale.
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.
Unique: Detects new topics by comparing current hotspot rankings against historical data, marking topics with significant rank increases as 🆕. Tracks emergence velocity to distinguish breaking news from sustained trends.
vs alternatives: More efficient than semantic similarity detection (no LLM overhead) and more accurate than simple first-appearance detection (accounts for re-emerging topics), but requires historical baseline data.
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.
Unique: Provides web-based configuration editor with real-time validation, regex preview, and schedule visualization. Enables non-technical users to configure TrendRadar without editing YAML files.
vs alternatives: More user-friendly than manual YAML editing and provides validation feedback, but adds operational complexity compared to file-based configuration.
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.
Unique: Uses LiteLLM abstraction layer to support any LLM provider (OpenAI, Anthropic, Ollama, local models) with single configuration, enabling provider switching without code changes. Caches analysis results to reduce redundant API calls and costs.
vs alternatives: More flexible than hardcoded OpenAI integration (supports any LiteLLM provider) and cheaper than dedicated sentiment analysis APIs (can use local models), but slower than rule-based sentiment analysis.
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).
Unique: Implements provider-agnostic translation via LiteLLM with automatic language detection, content-based caching, and batch request optimization. Stores translations alongside originals for bilingual report generation.
vs alternatives: More flexible than dedicated translation APIs (supports any LiteLLM provider) and cheaper than commercial translation services when using local models, but slower than specialized translation APIs.
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.
Unique: Implements atomic message batching across 9+ heterogeneous channels with provider-specific formatters and constraint-aware truncation. Single configuration enables simultaneous delivery to WeChat, WeWork, Feishu, Telegram, Email, ntfy, Bark, Slack, etc. without code changes.
vs alternatives: Supports more channels (especially Chinese platforms like WeWork, Feishu) than generic notification services, and batching reduces API calls and spam compared to per-item notifications.
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.
Unique: Implements full MCP server exposing trend data and analysis tools to LLM agents, enabling conversational queries and multi-turn analysis workflows. Maintains state across requests and supports complex tool invocations (filtering, analysis, report generation).
vs alternatives: Enables conversational access to trends (vs. API-only access) and integrates with LLM agent workflows (vs. standalone tools), but adds operational complexity compared to simple REST APIs.
+4 more capabilities