| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Implements a three-tier task hierarchy (ideas → epics → tasks) that enables progressive refinement of work items from high-level concepts to actionable tasks. The system maintains parent-child relationships through a graph-like data structure, allowing users to expand or collapse task trees and track completion status at each level. This architecture supports both top-down planning (breaking ideas into epics into tasks) and bottom-up aggregation (rolling up task completion to parent epic status).
Unique: Uses a fixed three-tier hierarchy (ideas → epics → tasks) rather than arbitrary nesting, which simplifies implementation and enforces a consistent planning discipline. The MCP integration allows this to be exposed as a tool-use capability to LLM agents, enabling AI-assisted task breakdown.
vs alternatives: Simpler and more opinionated than Jira's flexible hierarchy, making it faster to adopt for teams that don't need complex custom workflows; MCP integration enables AI agents to decompose tasks autonomously.
Renders a terminal-based dashboard that displays the hierarchical task tree with visual indicators for status, priority, and completion. The implementation uses ANSI color codes and box-drawing characters to create an interactive tree view that can be navigated and expanded/collapsed. The dashboard updates in real-time as tasks are created, modified, or completed, providing immediate visual feedback without requiring page refreshes or external tools.
Unique: Implements a native terminal dashboard rather than relying on web UI or external tools, using ANSI rendering for fast, lightweight visualization. The MCP integration allows the dashboard to be driven by LLM agents that can update tasks programmatically while the user watches the tree update in real-time.
vs alternatives: Faster and more accessible than web-based project managers for terminal-native developers; lighter weight than Asana or Monday.com, with zero external dependencies for visualization.
Exposes task management operations (create idea, create epic, create task, update status, delete task) as MCP tools that can be called by LLM agents through a standardized function-calling interface. Each tool has a defined schema (JSON Schema) specifying required parameters, types, and validation rules. The MCP server handles tool invocation, validates inputs, executes the operation, and returns structured results that the agent can reason about and chain into subsequent operations.
Unique: Implements MCP tool-use as the primary interface for task operations, rather than a secondary feature. This makes the system natively agentic — tasks can be created and managed by AI without human intervention, with the CLI dashboard providing human visibility into agent-driven changes.
vs alternatives: More integrated with AI workflows than traditional REST APIs; MCP protocol is lighter and more agent-friendly than webhook-based integrations or polling mechanisms.
Maintains completion state for individual tasks (not started, in progress, completed) and automatically aggregates status up the hierarchy to calculate epic and idea completion percentages. The system uses a bottom-up calculation model where parent status is derived from child task completion counts. Status changes are propagated immediately, allowing dashboards and agents to see real-time progress metrics without manual updates.
Unique: Uses automatic bottom-up aggregation rather than requiring manual parent status updates. This reduces user burden and ensures consistency, but also means the system cannot represent partial progress or weighted effort.
vs alternatives: Simpler and faster than effort-based burndown tracking; automatic aggregation reduces manual overhead compared to tools that require explicit parent status updates.
Stores task hierarchies and metadata in a persistent backend (likely JSON files or SQLite database based on typical MCP patterns) that survives process restarts. The system implements CRUD operations (create, read, update, delete) that serialize/deserialize task objects to/from storage. Concurrent access is handled through file locking or transaction isolation, ensuring data consistency when multiple clients or agents access the same project.
Unique: Implements local-first persistence without requiring external cloud services or databases. This keeps the system lightweight and self-contained, but also means users are responsible for backup and sync.
vs alternatives: More portable and privacy-friendly than cloud-based tools; no vendor lock-in or external dependencies, but requires manual backup/sync management.
Stores and manages additional task attributes beyond title and status, such as priority level (low, medium, high, critical), assignee, due date, and custom tags or labels. The system allows filtering and sorting tasks by these attributes, enabling users and agents to focus on high-priority or overdue work. Metadata is included in MCP tool schemas, allowing agents to set these properties when creating or updating tasks.
Unique: Integrates priority and assignment metadata directly into the MCP tool schema, allowing agents to set these properties programmatically. This enables AI-driven task prioritization and workload balancing.
vs alternatives: Simpler than Jira's custom field system; metadata is built-in rather than optional, ensuring consistent task information across the system.
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 Project Manager at 20/100.
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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
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