| Ecosystem | 0 | 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 9 decomposed | 12 decomposed |
| Times Matched | 0 | 0 |
Maintains a single authoritative servers.json file that defines all available MCP servers, their execution commands, configuration schemas, and runtime parameters. The registry uses a hub-and-spoke architecture where this central JSON file serves as the source of truth consumed by both the web application frontend and external MCP clients, enabling standardized server discovery and configuration across the ecosystem.
Unique: Uses a single public/servers.json file as the authoritative registry consumed by both web UI and MCP clients, with GitHub PR workflow for community contributions, rather than a database-backed registry with API endpoints
vs alternatives: Simpler than database-backed registries for open-source communities because it leverages GitHub's built-in review and version control, but trades real-time updates for operational simplicity
Supports execution of MCP servers across multiple runtime environments (Node.js via npx, Python via uvx/python, and direct command execution) by storing runtime-agnostic command templates in the registry. Each server definition includes a command string that specifies the execution method, and the system resolves parameters at runtime to generate the final executable command, enabling servers written in different languages to coexist in a unified directory.
Unique: Implements runtime-agnostic command templating with {{paramName@paramType::description}} syntax that allows a single registry entry to support execution across npx, uvx, python, and node runtimes without language-specific adapters
vs alternatives: More flexible than language-specific registries because it treats all servers as command-line executables, but requires clients to have all runtimes installed rather than providing containerized execution
Enables dynamic server configuration by defining user-facing parameters using a template syntax ({{paramName@paramType::description}}) that gets resolved at installation time. The system parses parameter definitions from server configurations, presents them to users through the web interface, collects their values, and substitutes them into command templates before execution, supporting API keys, file paths, and other runtime-specific configuration.
Unique: Uses a declarative {{paramName@paramType::description}} syntax embedded in server definitions to define parameters, which the web UI parses and presents as form fields, then substitutes back into command templates at installation time
vs alternatives: Simpler than environment variable management because parameters are collected through the UI and substituted directly into commands, but less secure than secret management systems because values may be exposed in command history
Provides a Next.js-based web application that consumes the servers.json registry and renders a searchable, filterable interface for discovering MCP servers. The application implements full-text search across server names and descriptions, category-based filtering, and a details dialog showing complete server metadata, enabling users to browse and understand available servers before installation.
Unique: Implements a Next.js-based static web application that renders the servers.json registry with client-side search and filtering, using React components for the main interface, search dialog, and server details modal
vs alternatives: More user-friendly than browsing raw JSON because it provides visual discovery and filtering, but less powerful than database-backed search because it lacks semantic understanding and ranking
Generates deep links using the app.5ire:// protocol that encode server configuration and parameters, allowing users to click an install button in the web UI and automatically trigger installation in compatible MCP clients (like 5ire). The system constructs deep links by serializing server metadata and resolved parameters into a URI that the client application can parse and execute.
Unique: Uses the app.5ire:// custom protocol scheme to create one-click installation links that encode server metadata and parameters, enabling seamless handoff from web discovery to client installation
vs alternatives: More seamless than copy-paste commands because users click a button and the client handles everything, but less portable than standardized protocols because it's tied to the 5ire client ecosystem
Implements a community-driven contribution model where developers submit new MCP servers by creating pull requests against the public/servers.json file. The system provides contribution guidelines, schema validation, and a review process that ensures quality control before servers are added to the registry, enabling decentralized community participation while maintaining data integrity.
Unique: Uses GitHub's native PR workflow as the contribution mechanism, with servers.json as the single source of truth that gets updated through merged PRs, rather than a separate contribution form or API endpoint
vs alternatives: More transparent and auditable than API-based submissions because the full history is visible in Git, but slower than automated systems because human review is required before each server goes live
Defines a standardized JSON schema for server entries that includes name, description, command template, parameter definitions, tags, and other metadata. Each server entry follows this schema, enabling consistent parsing and presentation across the web UI and client applications. The schema documentation provides clear guidance on required fields, parameter syntax, and configuration patterns.
Unique: Defines a lightweight, human-readable JSON schema for server entries that includes command templates, parameter definitions with type annotations, and metadata, documented through README examples rather than formal JSON Schema
vs alternatives: More accessible to non-technical contributors than formal JSON Schema because it uses simple examples, but less rigorous for validation because there's no automated schema enforcement
Implements OpenGraph and meta tags in the Next.js app/layout.tsx to optimize the web application for search engine indexing and social media sharing. The metadata includes title, description, and image tags that enable rich previews when the MCPSvr site is shared on social platforms, improving discoverability and click-through rates from external sources.
Unique: Uses Next.js app/layout.tsx metadata configuration with OpenGraph tags to optimize the MCPSvr platform for social sharing and search engine indexing, with the title 'MCPServer - Discover Exceptional MCP Servers'
vs alternatives: More maintainable than manually adding meta tags to HTML because it's centralized in the layout component, but less sophisticated than dynamic per-page metadata because all pages share the same tags
+1 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 mcpsvr at 31/100.
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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