| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
This capability aggregates threat-related news from various sources in real-time using a microservices architecture that allows for modular integration of different news feeds. It employs a pub/sub pattern to disseminate updates to connected clients efficiently, ensuring that users receive the latest information without significant delays. The system is designed to handle high throughput, making it suitable for environments where timely information is critical.
Unique: Utilizes a microservices architecture to allow for flexible integration of multiple news sources, enabling real-time updates.
vs alternatives: More responsive than traditional polling methods, as it uses a pub/sub model for immediate updates.
This capability allows users to set up customizable alerts based on specific threat keywords or categories. It uses a rule-based engine that evaluates incoming news items against user-defined criteria, triggering notifications through various channels such as email or messaging apps. The design supports dynamic rule updates, enabling users to adapt to emerging threats quickly.
Unique: Incorporates a dynamic rule engine that allows for real-time updates to alert criteria, enhancing responsiveness to new threats.
vs alternatives: More flexible than static alert systems, allowing users to modify rules on-the-fly.
This capability provides an API for accessing aggregated threat intelligence data, allowing developers to integrate threat news into their applications seamlessly. It follows RESTful principles, ensuring that the API is easy to use and supports standard HTTP methods for data retrieval and manipulation. The API is designed with versioning to ensure backward compatibility as new features are added.
Unique: Designed with a focus on RESTful principles and backward compatibility, making it easy for developers to adopt and integrate.
vs alternatives: More user-friendly than SOAP-based APIs, providing a simpler integration experience.
This capability allows users to analyze historical threat data to identify trends and patterns over time. It leverages time-series databases to store and query data efficiently, enabling users to run complex queries and generate visualizations of threat evolution. The system supports various analytical functions, such as aggregations and filtering, to help users derive insights from the data.
Unique: Utilizes time-series databases for efficient storage and querying of historical threat data, enabling detailed trend analysis.
vs alternatives: More efficient for time-based queries compared to traditional relational databases.
This capability enables users to collaboratively report and discuss threats within a shared platform. It employs a real-time collaboration framework that allows multiple users to edit and comment on threat reports simultaneously. The system uses WebSocket connections to ensure that updates are reflected instantly across all users' interfaces, promoting teamwork and rapid response.
Unique: Incorporates real-time collaboration features using WebSockets, allowing for instant updates and teamwork.
vs alternatives: More interactive than traditional document sharing tools, facilitating immediate feedback and discussion.
Executes SQL queries against Supabase PostgreSQL instances through the Model Context Protocol, translating natural language or structured query requests into parameterized SQL statements. Uses MCP's tool-calling interface to expose database operations as callable functions with schema validation, enabling LLM agents to perform CRUD operations, joins, and aggregations with automatic connection pooling and credential management through Supabase client SDK.
Unique: Exposes Supabase PostgreSQL as MCP tools with automatic credential injection from Supabase client SDK, eliminating manual connection string management and enabling seamless LLM-to-database queries within Claude or compatible agents
vs alternatives: Tighter integration than generic SQL MCP servers because it leverages Supabase's built-in authentication and connection pooling rather than requiring separate database credential configuration
Exposes Supabase Auth session state and user metadata through MCP tools, allowing agents to inspect current authentication context, retrieve user profiles, and trigger auth-related operations. Integrates with Supabase's JWT-based auth system to validate sessions and access user claims without re-authenticating, using the Supabase client's built-in session management.
Unique: Integrates Supabase's JWT-based auth system directly into MCP tool interface, allowing agents to inspect and act on auth state without managing separate credential stores or re-authentication flows
vs alternatives: More seamless than generic auth MCP servers because it leverages Supabase's built-in session management and avoids redundant credential passing between agent and auth system
Invokes Supabase Edge Functions (serverless TypeScript/JavaScript functions) through MCP tools, passing parameters and receiving results with optional streaming support. Uses Supabase's edge function HTTP API to trigger functions with automatic authentication headers and response parsing, enabling agents to execute custom business logic without embedding it in the agent itself.
Supabase scores higher at 42/100 vs threatnews1 at 23/100. threatnews1 leads on ecosystem, while Supabase is stronger on quality.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
Unique: Exposes Supabase Edge Functions as MCP tools with automatic authentication and response parsing, allowing agents to invoke custom serverless logic without managing HTTP clients or credential injection
vs alternatives: More integrated than generic HTTP MCP tools because it handles Supabase-specific authentication, error handling, and response formatting automatically
Subscribes to real-time changes on Supabase tables through MCP's event streaming interface, using Supabase's PostgreSQL LISTEN/NOTIFY mechanism to push INSERT, UPDATE, and DELETE events to agents. Maintains persistent WebSocket connections and filters events by table and row-level policies, enabling agents to react to database changes without polling.
Unique: Bridges Supabase's PostgreSQL LISTEN/NOTIFY real-time system with MCP's tool interface, enabling agents to subscribe to database changes without managing WebSocket connections or event serialization
vs alternatives: More efficient than polling-based approaches because it uses Supabase's native real-time infrastructure rather than repeated database queries
Manages files in Supabase Storage buckets through MCP tools, supporting upload, download, list, and delete operations with automatic authentication and path-based access control. Uses Supabase's S3-compatible storage API with built-in support for public/private buckets and signed URLs for temporary access, enabling agents to handle file I/O without managing cloud storage credentials.
Unique: Exposes Supabase Storage's S3-compatible API as MCP tools with automatic authentication and signed URL generation, eliminating the need for agents to manage cloud storage credentials or generate temporary access tokens
vs alternatives: More integrated than generic S3 MCP tools because it leverages Supabase's built-in bucket policies and authentication rather than requiring separate AWS credentials
Performs semantic similarity searches on vector embeddings stored in Supabase PostgreSQL using pgvector extension, translating natural language queries into embedding vectors and executing cosine/L2 distance searches. Integrates with embedding providers (OpenAI, Cohere) or uses pre-computed embeddings, enabling agents to retrieve semantically similar documents or records without full-text search limitations.
Unique: Integrates pgvector directly into MCP tools with automatic embedding generation and distance calculation, enabling agents to perform semantic search without managing separate vector database infrastructure
vs alternatives: More efficient than external vector databases (Pinecone, Weaviate) for Supabase users because it colocates embeddings with relational data, reducing network latency and simplifying data synchronization
Exposes Supabase database schema information through MCP tools, allowing agents to discover table structures, column types, constraints, and relationships without manual schema documentation. Queries PostgreSQL information_schema and Supabase metadata tables to dynamically generate schema descriptions, enabling agents to construct valid queries and understand data relationships.
Unique: Queries Supabase's PostgreSQL information_schema directly through MCP tools, enabling agents to dynamically discover and adapt to database schemas without pre-configured schema definitions
vs alternatives: More flexible than static schema definitions because it reflects live database state, including recent migrations or schema changes
Enforces Supabase Row-Level Security policies within agent queries, ensuring that agents can only access rows permitted by RLS rules defined in the database. Evaluates policies based on authenticated user context (JWT claims, user ID) and applies WHERE clause filters automatically, preventing unauthorized data access at the database layer rather than application layer.
Unique: Delegates authorization enforcement to PostgreSQL RLS policies rather than implementing authorization in agent code, ensuring that data access rules are centralized and cannot be bypassed by agent logic
vs alternatives: More secure than application-level authorization because RLS is enforced at the database layer, preventing accidental data leaks even if agent code has bugs
+1 more capabilities