| 0 |
| 0 |
| Ecosystem | 1 | 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 16 decomposed | 12 decomposed |
| Times Matched | 0 | 0 |
Exposes 90+ tools across 12 Google Workspace services (Gmail, Drive, Calendar, Docs, Sheets, Slides, Forms, Tasks, Chat, Custom Search, Contacts, Apps Script) through a unified MCP protocol interface. Uses a ToolTierLoader system (core/tool_tier_loader.py) that dynamically imports tool modules based on CLI-specified tiers (core/extended/complete), allowing selective API exposure to manage quota consumption and complexity. Tools are registered in a dictionary mapping (main.py 176-187) and loaded at server startup, with each service module implementing standardized tool patterns for consistent MCP schema generation.
Unique: Implements a three-tier tool loading system (core/extended/complete) via ToolTierLoader that allows fine-grained control over API surface exposure at server startup, preventing quota exhaustion in multi-user deployments. Most MCP servers expose all tools statically; this design enables quota-aware selective loading without code changes.
vs alternatives: Provides more granular quota control than generic MCP servers like Anthropic's MCP implementations, which typically expose all available tools without tier-based filtering.
Implements dual OAuth authentication modes (OAuth 2.0 legacy flow and OAuth 2.1 with session management) via service authentication decorators that inject credentials into tool execution contexts. Credentials are stored persistently (location configurable via storage backend) and session context is maintained across tool calls, eliminating per-call re-authentication. The authentication system (core/auth.py) handles token refresh, expiration, and multi-user credential isolation in cloud deployments. Single-user mode (--single-user flag) uses local credential storage; multi-user mode requires external session storage (Redis, database) for credential isolation.
Unique: Supports both OAuth 2.0 legacy and OAuth 2.1 flows with automatic session context injection via service authentication decorators, enabling credential reuse across tool calls without explicit token passing. Includes configurable storage backends for multi-user credential isolation, distinguishing it from single-user-only MCP implementations.
vs alternatives: Provides multi-user credential isolation that generic MCP servers lack, and supports OAuth 2.1 (modern standard) alongside legacy OAuth 2.0, making it suitable for both legacy and modern Google Workspace deployments.
Provides 6+ Chat tools for sending messages to spaces and direct messages, retrieving conversation history, and managing chat spaces. Tools support message formatting (bold, italic, links) and file attachments. Chat operations include creating spaces, adding members, and retrieving message threads. The Chat module (tools/chat.py) handles message threading and implements pagination for conversation history. Supports both direct messages (DM) and space-based conversations.
Unique: Implements message threading and space-based conversation management with support for both direct messages and group spaces. Includes message formatting and attachment support with pagination for conversation history.
vs alternatives: Supports both direct messages and space-based conversations that many chat tools limit to one or the other; integrates with Google Workspace for unified team communication.
Implements dual transport modes for MCP server deployment: stdio (for local/desktop use) and streamable-http (for cloud/multi-user deployments). The SecureFastMCP class (core/server.py) extends FastMCP and configures transport based on CLI flag (--transport). Stdio mode pipes JSON-RPC requests/responses through standard input/output for Claude Desktop integration. Streamable-http mode exposes an HTTP server (configurable port) for remote client connections. Both modes support the same MCP protocol and tool registry. The server initialization (main.py) handles transport selection and startup.
Unique: Supports dual transport modes (stdio and streamable-http) from a single codebase, enabling both local desktop and cloud deployments without code changes. Uses FastMCP's transport abstraction to handle protocol differences transparently.
vs alternatives: More flexible than single-transport MCP servers; supports both local (Claude Desktop) and cloud (HTTP) deployments, making it suitable for diverse deployment scenarios.
Implements automatic retry logic with exponential backoff for transient API failures (rate limits, quota exhaustion, temporary service unavailability). The error handling system (core/error_handling.py or integrated in tool modules) detects quota-related errors from Google APIs and automatically retries with increasing delays (1s, 2s, 4s, 8s, etc.). Maximum retry attempts are configurable (default 3). Non-transient errors (authentication failures, invalid parameters) fail immediately without retry. Retry metadata is included in error responses to inform clients of retry attempts.
Unique: Implements exponential backoff retry logic specifically tuned for Google API quota limits (429 status codes), with configurable max attempts and automatic detection of transient vs permanent errors. Includes retry metadata in responses for observability.
vs alternatives: More sophisticated than simple retry loops; uses exponential backoff to reduce load during quota exhaustion and distinguishes transient from permanent errors to avoid wasted retries.
Exposes 2+ Custom Search tools that integrate with Google Custom Search Engine (CSE) for web search and result ranking. Tools support search queries with optional filters (site:, filetype:) and return ranked results with metadata (title, URL, snippet, rank). The Custom Search module (tools/custom_search.py) uses the Custom Search API for server-side query execution and result ranking. Results are limited to top 10 by default (configurable). Supports both web search and image search modes.
Unique: Integrates Google Custom Search Engine (CSE) for web search with result ranking and snippet extraction. Supports site: and filetype: filters for targeted searches. Limited to top 10 results but provides high-quality ranked results.
vs alternatives: Uses Google's Custom Search Engine for high-quality ranked results compared to generic web search APIs; supports domain-specific and file-type filtering for targeted searches.
Provides 4+ Contacts tools for retrieving contact information from Google Contacts directory, including name, email, phone, and organization metadata. Tools support contact search by name or email and batch retrieval of contact lists. The Contacts module (tools/contacts.py) uses the People API to access contact data with structured metadata extraction. Supports filtering by contact group (personal, work, etc.). Contact creation and editing are not supported (read-only access).
Unique: Provides read-only access to Google Contacts directory via the People API with structured metadata extraction (name, email, phone, organization, title). Supports contact search by name/email and filtering by contact group.
vs alternatives: Integrates with Google Contacts for unified contact management; provides structured metadata extraction that generic contact tools may not expose.
Exposes 3+ Apps Script tools for executing Apps Script functions and managing script deployments. Tools support function execution with parameters and return value retrieval. The Apps Script module (tools/apps_script.py) uses the Apps Script API to execute scripts and retrieve execution results. Supports both synchronous and asynchronous function execution. Script deployments can be listed and managed. Execution errors are captured and returned with stack traces.
Unique: Integrates Google Apps Script API for executing custom business logic functions, enabling extension of Google Workspace capabilities with custom automation. Supports both synchronous and asynchronous execution with error capture.
vs alternatives: Enables custom business logic integration that generic Google Workspace tools cannot provide; allows reuse of existing Apps Script automation with AI agents.
+8 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 google_workspace_mcp at 40/100. google_workspace_mcp leads on quality, while TrendRadar is stronger on adoption.
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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