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| Ecosystem | 1 | 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 13 decomposed | 12 decomposed |
| Times Matched | 0 | 0 |
Executes PostgreSQL queries against Supabase databases with automatic risk classification into three tiers: Safe (SELECT-only, always allowed), Write (INSERT/UPDATE/DELETE, requires unsafe mode), and Destructive (DROP/CREATE, requires unsafe mode + explicit confirmation). The system parses incoming SQL, classifies operations by AST analysis, and enforces execution gates based on the current safety mode setting, preventing accidental schema destruction while enabling controlled data mutations.
Unique: Implements a three-tier safety classification system (Safe/Write/Destructive) with explicit confirmation gates for destructive operations, integrated directly into the MCP tool invocation layer rather than as a separate middleware. This allows LLM agents to understand safety constraints at tool-call time and request user confirmation before executing risky operations.
vs alternatives: Safer than raw Supabase client libraries for agentic use because it enforces safety gates at the MCP protocol boundary, preventing LLMs from executing destructive SQL without explicit human confirmation, whereas direct client libraries rely on application-level safeguards that agents can bypass.
Automatically versions and tracks database schema changes by capturing migration metadata (timestamp, operation type, SQL statement) whenever destructive or schema-modifying operations execute. The system maintains a migration history log that can be queried to understand schema evolution, rollback points, and audit trails of who changed what when. This integrates with Supabase's native migration system to ensure version consistency across environments.
Unique: Integrates migration versioning directly into the MCP tool execution layer, automatically capturing and storing migration metadata whenever schema changes occur, rather than requiring developers to manually create migration files. This creates an implicit audit trail of all schema changes made through the chat interface.
vs alternatives: More transparent than manual migration management because every schema change is automatically versioned and logged, whereas traditional Supabase workflows require developers to manually create and track migration files, which can be forgotten or inconsistently documented.
Catches and handles exceptions from database operations, Management API calls, and Auth SDK invocations, preserving error context (stack trace, operation details, input parameters) and returning user-friendly error messages. The system distinguishes between recoverable errors (connection timeouts, rate limits) and fatal errors (authentication failures, invalid SQL), and provides actionable error messages that help developers understand what went wrong. This prevents cryptic error messages from reaching users and enables better debugging.
Unique: Implements custom exception handling that preserves error context (operation details, input parameters) while sanitizing sensitive information before returning to users. This enables detailed debugging without leaking credentials or internal system details.
vs alternatives: More helpful than raw exception messages because it provides context-specific guidance (e.g., 'Invalid credentials — check SUPABASE_SERVICE_ROLE_KEY environment variable'), whereas raw exceptions often lack actionable information.
Provides Dockerfile and Docker Compose configuration for containerizing the MCP server, enabling deployment in Docker environments with environment variable injection for credentials. The system builds a Python 3.12 container with all dependencies, exposes the stdio interface for MCP clients, and supports environment variable configuration for different deployment scenarios. This enables easy deployment to cloud platforms (AWS, GCP, Azure) and local Docker environments without manual setup.
Unique: Provides production-ready Dockerfile and Docker Compose configuration that handles Python dependency installation, environment variable injection, and stdio interface exposure for MCP clients. This enables one-command deployment to container environments.
vs alternatives: More portable than manual installation because Docker ensures consistent environments across development, staging, and production, whereas manual installation can have environment-specific issues (Python version, dependency conflicts).
Provides a testing framework with mock Supabase clients (database, Management API, Auth SDK) for unit testing without real Supabase credentials, and integration tests that run against a real Supabase instance. The system uses pytest for test execution, fixtures for test setup/teardown, and parametrized tests for testing multiple scenarios. This enables developers to test MCP tools locally without requiring a Supabase account and to verify integration with real Supabase services in CI/CD pipelines.
Unique: Provides both unit tests with mock clients and integration tests with real Supabase instances, enabling developers to test locally without credentials and verify integration in CI/CD pipelines. This dual approach balances test speed (mocks) with confidence (integration tests).
vs alternatives: More comprehensive than manual testing because automated tests catch regressions and edge cases, whereas manual testing is error-prone and doesn't scale as the codebase grows.
Provides MCP tool bindings for all Supabase Management API endpoints (project management, database configuration, auth settings, etc.) with automatic risk assessment and safety controls. The system maps Management API operations to MCP tools, injects project references automatically, classifies each endpoint by risk level (read-only vs destructive), and enforces safety gates similar to SQL execution. This enables chat-driven management of Supabase project infrastructure without requiring manual API calls or authentication.
Unique: Automatically injects project references and applies the same three-tier safety classification system (Safe/Write/Destructive) to Management API endpoints as it does to SQL queries, creating a unified safety model across database and infrastructure operations. This prevents accidental project-level destructive operations (e.g., database resets) without explicit confirmation.
vs alternatives: More accessible than raw Management API clients because it abstracts authentication, project reference injection, and safety gates into MCP tools that LLMs can safely invoke, whereas direct API clients require manual authentication handling and provide no guardrails against destructive operations.
Exposes Supabase Auth Admin SDK methods as MCP tools, enabling chat-driven user management operations including user creation, updates, deletion, authentication operations (magic links, password recovery), and MFA management. The system wraps Auth Admin SDK calls with proper error handling, validates input parameters, and integrates with the safety system to require confirmation for destructive user operations (deletion, password resets). This allows developers to manage authentication state and user accounts without leaving their IDE.
Unique: Wraps the Supabase Auth Admin SDK with MCP tool bindings and integrates user deletion/password reset operations into the safety system, requiring explicit confirmation before destructive auth operations. This prevents LLMs from accidentally deleting user accounts or forcing password resets without human approval.
vs alternatives: Safer than direct Auth Admin SDK usage in agentic contexts because it enforces confirmation gates for destructive user operations, whereas raw SDK clients allow agents to delete users or reset passwords without safeguards, risking data loss and user disruption.
Provides MCP tools to query Supabase logs across multiple collections (postgres, api_gateway, auth, realtime, etc.) with filtering by time range, search text, and custom criteria. The system constructs log queries using Supabase's log API, handles pagination for large result sets, and returns structured log entries as JSON objects. This enables developers to troubleshoot issues, monitor application behavior, and analyze performance without leaving their IDE or switching to the Supabase dashboard.
Unique: Integrates Supabase's multi-collection log API into MCP tools with automatic pagination and structured result formatting, allowing LLM agents to query logs conversationally without understanding the underlying log API schema. This abstracts log collection names, filter syntax, and pagination logic into simple tool parameters.
vs alternatives: More accessible than raw log API clients because it provides high-level filtering and search without requiring knowledge of Supabase's log query syntax, whereas direct API clients require developers to construct complex filter objects and handle pagination manually.
+5 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 supabase-mcp-server at 36/100. supabase-mcp-server leads on quality, while TrendRadar is stronger on adoption and ecosystem.
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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