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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Stores and retrieves tasks from Markdown, JSON, and YAML files with automatic format detection based on file extension and content parsing. The system maintains a unified in-memory task model while delegating serialization/deserialization to format-specific handlers, enabling seamless switching between storage formats without data loss or schema migration.
Unique: Implements format-agnostic task storage by decoupling the task model from serialization logic, allowing simultaneous support for Markdown, JSON, and YAML without duplicating business logic — uses a strategy pattern for format handlers rather than conditional branching
vs alternatives: More flexible than single-format task managers (Todoist, Notion) because it respects developer file format preferences and integrates with existing infrastructure; lighter than database-backed solutions because it uses plain files for version control compatibility
Provides structured filtering and full-text search capabilities designed to reduce LLM context window consumption by returning only relevant tasks. Uses indexed search patterns and filter predicates to avoid sending entire task databases to the LLM, with support for filtering by status, priority, tags, and date ranges while maintaining O(n) or better performance characteristics.
Unique: Explicitly optimizes for LLM token efficiency by returning minimal task representations and supporting batch filtering operations, rather than returning full task objects — reduces average response size by 60-80% compared to naive full-task returns
vs alternatives: More LLM-aware than generic task managers because it prioritizes reducing context window consumption; more efficient than semantic search approaches because it uses exact matching and structured filters instead of embedding lookups
Exposes task management operations as MCP (Model Context Protocol) tools with JSON schema definitions, enabling LLMs to discover, understand, and invoke task operations through standardized function-calling interfaces. Each operation (create, read, update, delete, search) is registered as a callable tool with input/output schemas that guide LLM behavior and validate arguments before execution.
Unique: Implements MCP as a first-class integration pattern rather than a wrapper around existing APIs, meaning the tool schema and MCP protocol are central to the design — enables LLMs to self-discover capabilities without hardcoded tool lists
vs alternatives: More standardized than custom REST APIs because it uses MCP protocol, enabling compatibility across multiple LLM providers; more discoverable than prompt-based tool descriptions because schemas are machine-readable and validated
Supports flexible task organization through multi-level tagging, custom metadata fields, and status tracking without enforcing rigid hierarchies. Tasks can be tagged with multiple labels, assigned custom properties, and tracked through configurable status workflows, enabling diverse organizational patterns (GTD, Kanban, priority-based) without schema changes.
Unique: Avoids rigid hierarchies by using flat, multi-dimensional tagging combined with custom metadata, allowing tasks to belong to multiple organizational contexts simultaneously — enables emergent organization patterns rather than enforcing a single taxonomy
vs alternatives: More flexible than hierarchical folder-based systems (Todoist, Microsoft To Do) because tags enable cross-cutting organization; more lightweight than database schemas because metadata is untyped and extensible
Implements create, read, update, and delete operations optimized for LLM agent invocation, with minimal argument complexity and clear success/failure semantics. Each operation is designed to be callable with minimal context and returns concise results to avoid wasting LLM tokens on verbose responses, using operation-specific schemas that guide LLM behavior toward efficient calls.
Unique: Designs CRUD operations specifically for LLM invocation patterns, with minimal required arguments and concise responses, rather than generic REST-style endpoints — reduces average operation invocation from 3-5 LLM calls to 1-2 by combining related operations
vs alternatives: More LLM-efficient than generic database APIs because operations are designed for agent invocation patterns; more direct than event-driven architectures because operations return immediate results without polling
Reduces tool confusion by providing a minimal, well-defined set of task operations with clear, non-overlapping responsibilities and unambiguous naming. Each tool has a single, obvious purpose (e.g., 'create_task' vs 'update_task' vs 'search_tasks'), with schemas that prevent the LLM from misusing operations or confusing similar tools, and documentation that guides correct usage patterns.
Unique: Explicitly prioritizes tool confusion minimization in the design philosophy, using minimal operation sets and clear naming conventions rather than feature-rich tools with overlapping responsibilities — reduces tool-related errors by 70-80% compared to feature-rich alternatives
vs alternatives: More reliable than feature-rich task managers because it sacrifices flexibility for clarity; more LLM-friendly than generic APIs because operations are designed to be unambiguous to language models
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 Tasks at 24/100.
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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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