| 0 |
| Ecosystem | 0 | 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 12 decomposed |
| Times Matched | 0 | 0 |
Provides a minimal reference implementation for bootstrapping a Model Context Protocol server with standard lifecycle hooks (startup, shutdown, request handling). Uses the MCP SDK to establish bidirectional communication channels between client and server, handling protocol negotiation, message routing, and graceful shutdown. The hello-world pattern demonstrates the foundational server setup that all MCP implementations must follow.
Unique: Provides the simplest possible MCP server skeleton using the official Anthropic SDK, making it the canonical starting point for understanding MCP architecture without framework overhead or opinionated patterns
vs alternatives: Simpler and more direct than building from raw JSON-RPC, and more focused than full-featured frameworks like LangChain's MCP integration
Enables declaring tools with structured schemas (name, description, input parameters) and exposing them through the MCP tools/list and tools/call endpoints. The implementation uses JSON Schema to define tool signatures, allowing clients to discover available tools and invoke them with type-safe parameters. This follows the MCP specification for tool exposure and enables Claude or other clients to understand and call custom functionality.
Unique: Uses the MCP protocol's standardized tool definition format (JSON Schema + metadata) rather than proprietary function-calling formats, enabling interoperability across any MCP-compatible client
vs alternatives: More portable than OpenAI function calling or Anthropic's native tool_use because it's client-agnostic; simpler than LangChain tool definitions because it's protocol-native
Implements the core MCP message dispatch loop that routes incoming JSON-RPC 2.0 requests to appropriate handler functions based on method name. Uses event-driven patterns to attach handlers for specific MCP methods (e.g., 'tools/list', 'tools/call') and automatically serializes responses back to JSON-RPC format. The routing layer abstracts protocol details from business logic, allowing developers to focus on handler implementation.
Unique: Provides transparent request routing that abstracts MCP protocol details, allowing handler functions to work with plain JavaScript objects rather than raw JSON-RPC envelopes
vs alternatives: Cleaner than manual JSON-RPC parsing; more lightweight than full HTTP frameworks like Express for protocol-specific routing
Establishes persistent bidirectional communication channels between MCP client and server using stdio or network transports. Handles connection lifecycle (initialization, heartbeat/keep-alive if needed, graceful closure) and ensures both client and server can initiate messages. The transport abstraction allows the same server code to work over stdio (for local integration), HTTP, or other protocols without code changes.
Unique: Abstracts transport details behind a unified interface, allowing the same MCP server implementation to work over stdio (for local Claude Desktop integration) or network protocols without modification
vs alternatives: More flexible than hardcoded HTTP servers; simpler than building custom socket management for each transport type
Ensures the server implementation follows the Model Context Protocol specification, including proper message formatting, required fields, error handling conventions, and capability negotiation. The hello-world template demonstrates correct protocol usage patterns that clients can rely on, serving as a reference for what compliant MCP servers should look like. This includes proper handling of protocol versions, required metadata, and standard response formats.
Unique: Serves as the canonical reference implementation for MCP specification compliance, maintained by Anthropic and used to validate client implementations
vs alternatives: More authoritative than third-party implementations because it's the official reference; more complete than minimal examples because it covers required protocol patterns
Packages the MCP server as an npm module with proper package.json configuration, entry points, and dependency declarations. Enables developers to install the hello-world template as a starting point via 'npm install @lobehub/mcp-hello-world' or use it as a reference. The package includes build scripts, TypeScript definitions (if applicable), and proper export configuration for both CommonJS and ES modules.
Unique: Published as an official npm package from @lobehub organization, making it discoverable and installable through standard JavaScript package management workflows
vs alternatives: More accessible than cloning from GitHub because it's in the npm registry; more discoverable than documentation-only examples
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 mcp-hello-world at 36/100.
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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.
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