| 0 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 11 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Executes targeted queries against the OmniFocus database through AppleScript/JXA automation, supporting field selection, filtering predicates, and sorting parameters to retrieve specific task subsets without full database export. The query_omnifocus tool implements a schema-based filtering interface that translates LLM parameters into AppleScript queries, enabling efficient retrieval of tasks by status, project, due date, and custom metadata without loading the entire database into context.
Unique: Implements query-time field selection and predicate-based filtering through AppleScript, avoiding full database export overhead that competitors like REST API wrappers would incur. Uses MCP schema validation to translate natural language filter parameters into AppleScript predicates, enabling LLMs to construct efficient queries without understanding AppleScript syntax.
vs alternatives: More efficient than dump_database for selective queries (avoids context bloat) and more flexible than static REST endpoints by supporting dynamic filter composition through MCP schema parameters.
Exports the entire OmniFocus database as a structured JSON snapshot through AppleScript automation, providing comprehensive access to all tasks, projects, folders, and metadata for holistic analysis or context-aware reasoning. The dump_database tool serializes the complete OmniFocus object hierarchy (projects, tasks, contexts, perspectives) into a single JSON payload, enabling LLMs to perform cross-cutting analysis, dependency detection, or full-system optimization in a single context window.
Unique: Provides complete OmniFocus state serialization through MCP, enabling LLMs to access the full task hierarchy in a single call rather than requiring multiple incremental queries. Uses AppleScript object traversal to recursively serialize nested project/task structures with all metadata (notes, due dates, custom fields, tags, completion status).
vs alternatives: More comprehensive than query_omnifocus for holistic analysis, but trades latency and context efficiency for completeness; better than manual OmniFocus export because it's programmatic and integrates directly into LLM reasoning loops.
Validates tool parameters against JSON schemas before execution, ensuring MCP protocol compliance and preventing invalid requests from reaching AppleScript execution. Each tool defines a schema that specifies required/optional parameters, types, and constraints (e.g., ISO 8601 date format, valid project names), and the MCP server validates incoming requests against these schemas before invoking handlers. This approach provides early error detection, clear error messages to LLM clients, and prevents malformed AppleScript commands from being generated.
Unique: Implements schema-based parameter validation at the MCP protocol level, leveraging the @modelcontextprotocol/sdk's built-in validation to ensure all tool requests conform to defined schemas before execution. Uses JSON schema definitions embedded in tool handlers to specify parameter constraints and provide clear error messages.
vs alternatives: More robust than runtime parameter checking and provides earlier error detection than AppleScript-level validation; differs from REST API frameworks by integrating validation into the MCP protocol layer, ensuring consistency across all tools.
Creates individual tasks in OmniFocus through AppleScript automation, accepting natural language task names and optional structured metadata (project assignment, due dates, tags, notes, priority flags) via MCP schema parameters. The add_omnifocus_task tool translates LLM-generated task specifications into AppleScript commands that instantiate new task objects with full metadata binding, enabling AI assistants to create tasks with context-aware properties without requiring users to manually enter details.
Unique: Integrates task creation with metadata binding through a single MCP call, allowing LLMs to specify project, due date, tags, and notes atomically rather than requiring separate API calls for each property. Uses AppleScript's object model to create task objects with properties set during instantiation, avoiding post-creation modification overhead.
vs alternatives: More flexible than OmniFocus's native quick-entry (supports programmatic metadata assignment) and more efficient than manual task creation; differs from REST-based task APIs by leveraging AppleScript's direct object access for atomic creation.
Creates new projects in OmniFocus with optional parent project assignment, enabling hierarchical task organization through AppleScript automation. The add_project tool accepts project names and parent project references, translating them into AppleScript commands that instantiate project objects with proper hierarchy binding, allowing AI assistants to organize tasks into logical groupings and sub-projects based on conversation context or task analysis.
Unique: Supports hierarchical project creation through parent project assignment in a single MCP call, enabling LLMs to establish multi-level project structures without sequential parent-then-child creation patterns. Uses AppleScript's project object model to bind parent-child relationships during instantiation.
vs alternatives: More direct than manual OmniFocus project creation and supports programmatic hierarchy binding; differs from flat task list APIs by enabling nested project structures that mirror organizational complexity.
Updates existing OmniFocus tasks or projects by modifying specific fields (name, due date, project assignment, tags, notes, completion status, priority flags) through AppleScript automation. The editItem tool accepts item IDs and a map of field updates, translating them into AppleScript property assignments that modify task/project objects in-place, enabling AI assistants to refine task details, reschedule work, or update metadata based on conversation context without full item replacement.
Unique: Implements field-level updates through AppleScript property assignment, allowing selective modification of task/project properties without full object replacement. Uses a schema-based update map to translate LLM-generated field changes into targeted AppleScript property setters, enabling efficient partial updates.
vs alternatives: More granular than full item replacement and more efficient than query-modify-replace patterns; differs from REST PATCH endpoints by leveraging AppleScript's direct property access for atomic field updates.
Removes tasks or projects from OmniFocus through AppleScript automation, supporting both individual item deletion and cascading removal of nested items (deleting a project removes all contained tasks). The removeItem tool accepts item IDs and executes AppleScript delete commands that remove objects from the OmniFocus database, enabling AI assistants to clean up completed work, archive obsolete projects, or reorganize task structures based on analysis or user intent.
Unique: Implements cascading deletion through AppleScript's object hierarchy, automatically removing nested items when a parent project is deleted. Uses direct AppleScript delete commands rather than marking items as deleted, ensuring permanent removal from the OmniFocus database.
vs alternatives: More direct than manual OmniFocus deletion and supports programmatic cascading removal; differs from soft-delete APIs by providing permanent removal, which is appropriate for task management but requires careful LLM validation to prevent accidental data loss.
Creates multiple tasks in OmniFocus in a single MCP call through AppleScript automation, accepting an array of task specifications with metadata (names, due dates, projects, tags, notes) and executing sequential AppleScript commands to instantiate all tasks atomically. The batch_add_items tool optimizes bulk task creation by reducing MCP round-trips and AppleScript process overhead compared to individual add_omnifocus_task calls, enabling AI assistants to populate projects with related tasks or implement task breakdowns in a single operation.
Unique: Implements batch task creation through sequential AppleScript execution within a single MCP call, reducing round-trip overhead compared to individual task creation calls. Uses array-based input schema to enable LLMs to specify multiple tasks with consistent metadata in a single request, optimizing for bulk operations.
vs alternatives: More efficient than repeated add_omnifocus_task calls for bulk operations (fewer MCP round-trips) but slower than true parallel execution; differs from REST batch APIs by leveraging AppleScript's sequential execution model within a single MCP invocation.
+3 more capabilities
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 OmniFocus-MCP at 32/100.
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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
+1 more capabilities