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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Exposes Routine's calendar system through MCP protocol, enabling LLM agents and tools to create, read, update, and delete calendar events programmatically. Implements MCP resource and tool handlers that translate natural language or structured requests into Routine API calls, with support for event metadata (title, time, attendees, description). The server acts as a bridge between MCP clients and Routine's backend, handling authentication and request serialization.
Unique: Implements MCP server pattern specifically for Routine's calendar system, enabling seamless LLM agent integration without requiring developers to build custom API wrappers — the MCP protocol standardizes how agents discover and invoke calendar operations
vs alternatives: Provides native MCP integration for Routine calendars, whereas generic REST API clients require developers to manually implement tool schemas and context management for LLM agents
Exposes Routine's task/todo system through MCP tools and resources, allowing agents to create, list, update, and complete tasks with properties like priority, due dates, and descriptions. Implements MCP tool handlers that translate task operations into Routine API calls, supporting task state transitions (open, in-progress, completed) and metadata queries. Agents can query task lists, filter by status or due date, and update task progress.
Unique: Wraps Routine's task API in MCP tool definitions, allowing LLM agents to discover and invoke task operations without hardcoded prompts — agents can introspect available task fields and operations at runtime
vs alternatives: Simpler than building custom task integrations with REST APIs because MCP standardizes tool discovery and invocation, reducing boilerplate in agent code
Exposes Routine's notes system through MCP resources and tools, enabling agents to create, read, update, and search notes with support for text content, metadata (tags, timestamps), and organization. Implements MCP resource handlers that map note IDs to content and tool handlers for note operations. Agents can store context, retrieve previous notes for reference, and organize notes with tags for later retrieval.
Unique: Integrates Routine's notes as MCP resources, allowing agents to treat notes as first-class context sources that can be discovered and loaded dynamically — agents can reference note IDs in prompts without pre-loading all content
vs alternatives: More integrated than generic note-taking APIs because MCP resource semantics allow agents to understand note structure and metadata natively, enabling smarter retrieval patterns
Implements the Model Context Protocol (MCP) server specification, exposing Routine capabilities as standardized MCP resources, tools, and prompts. The server handles MCP client connections, serializes requests/responses in JSON-RPC format, and manages authentication with Routine's backend. Implements MCP tool definitions with JSON schemas for calendar, task, and note operations, enabling any MCP-compatible client (Claude Desktop, custom runners) to discover and invoke Routine features.
Unique: Implements full MCP server specification with tool and resource handlers, enabling Routine to be discovered and used by any MCP-compatible client — the server abstracts Routine's REST API behind MCP's standardized interface
vs alternatives: More flexible than direct API integration because MCP decouples clients from Routine's implementation details, allowing multiple tools and agents to interact with Routine through a single standardized server
Handles authentication with Routine's backend API, managing credentials (tokens, OAuth) and maintaining authenticated sessions for MCP tool invocations. The server stores and refreshes credentials, implements error handling for auth failures, and ensures all downstream Routine API calls are properly authenticated. Supports credential configuration via environment variables or configuration files.
Unique: Centralizes credential management within the MCP server, allowing clients to invoke Routine operations without handling authentication directly — credentials are managed server-side, reducing exposure in client code
vs alternatives: Safer than embedding credentials in client code because the MCP server acts as a credential broker, isolating sensitive tokens from agent implementations
Defines JSON schemas for all Routine operations (calendar, task, notes) exposed as MCP tools, enabling clients to discover available operations, required parameters, and expected outputs at runtime. The server implements MCP's tools/list and tools/call handlers, providing schema introspection so clients can generate appropriate prompts and validate inputs before invocation. Schemas include descriptions, parameter types, and constraints.
Unique: Exposes Routine operations as discoverable MCP tools with full JSON schemas, allowing agents to understand available operations and constraints without hardcoded knowledge — schemas enable dynamic tool selection and parameter validation
vs alternatives: More flexible than static tool definitions because schema-based discovery allows agents to adapt to new Routine features or operations without code changes
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 Routine 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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