| 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
This capability allows users to manage tasks in real-time by integrating with various project management tools using the Model Context Protocol (MCP). It employs a publish-subscribe pattern to ensure that updates to tasks are propagated instantly across connected clients, enabling seamless collaboration. The architecture supports multiple integrations, allowing users to connect their preferred tools without extensive configuration.
Unique: Utilizes a publish-subscribe model for real-time updates, ensuring that all connected clients receive immediate notifications of task changes.
vs alternatives: More responsive than traditional polling methods used by other kanban tools, providing instant updates without delay.
This capability enables users to define and automate workflows based on specific triggers and actions within the kanban board. It leverages a rule-based engine that allows users to create custom rules for task transitions, notifications, and integrations with external services. The system is designed to be user-friendly, allowing non-technical users to set up complex workflows without coding.
Unique: Features a user-friendly rule-based engine that allows non-technical users to create complex workflows without coding.
vs alternatives: More accessible than traditional scripting-based automation tools, enabling broader user adoption.
This capability provides users with a consolidated view of multiple kanban boards across different projects. It aggregates data from various sources and presents it in a unified dashboard, allowing users to track progress and identify bottlenecks. The implementation uses a microservices architecture to fetch and display data from multiple kanban servers, ensuring scalability and performance.
Unique: Employs a microservices architecture to aggregate and display data from multiple kanban boards, ensuring high performance and scalability.
vs alternatives: Offers a more comprehensive view than single-board tools, allowing for better oversight of project health.
This capability utilizes machine learning algorithms to analyze task data and suggest prioritization based on various factors such as deadlines, dependencies, and team workload. The engine continuously learns from user interactions and adapts its recommendations over time, providing increasingly relevant insights. It integrates seamlessly with the kanban board to update task priorities automatically.
Unique: Incorporates machine learning to dynamically suggest task priorities based on real-time data and user behavior.
vs alternatives: More adaptive than static prioritization methods, providing tailored recommendations that evolve with team needs.
This capability allows users to generate detailed reports and analytics on task performance, team productivity, and project timelines. It uses data visualization libraries to create interactive charts and graphs that can be customized according to user preferences. The reporting engine pulls data from the kanban boards and processes it in real-time, ensuring that insights are always up-to-date.
Unique: Utilizes real-time data processing and advanced visualization techniques to provide up-to-date insights into project performance.
vs alternatives: More interactive and customizable than standard reporting tools, enhancing user engagement with data.
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 kanban at 22/100. kanban 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