| 1 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
This capability allows users to perform bulk operations on emails by leveraging the Gmail API's batch processing feature. It uses a transactional approach to ensure that multiple email modifications, such as marking as read, archiving, or deleting, can be executed in a single API call, reducing the number of requests and improving efficiency. The implementation is designed to handle large datasets while maintaining user context and minimizing API rate limits.
Unique: Utilizes batch processing capabilities of the Gmail API to optimize multiple email actions in a single request, reducing overhead.
vs alternatives: More efficient than traditional methods by minimizing API calls and handling large datasets effectively.
This capability enables users to search for emails dynamically using various query parameters such as sender, subject, and date. It employs the Gmail API's advanced search operators, allowing for complex queries that return filtered results based on user-defined criteria. The implementation is designed to provide fast and relevant results by indexing emails and optimizing search queries for performance.
Unique: Leverages advanced search operators of the Gmail API to allow for complex and efficient email retrieval based on user-defined criteria.
vs alternatives: Faster and more flexible than standard email clients due to its use of indexed search capabilities.
This capability allows users to create draft emails automatically by utilizing templates and predefined content. It integrates with the Gmail API to save drafts directly to the user's account, using a context-aware approach to fill in dynamic fields such as recipient addresses and subject lines. The implementation is designed to streamline the drafting process, making it easier for users to generate consistent email content quickly.
Unique: Utilizes a context-aware template system that allows for dynamic content insertion, making email drafting more efficient.
vs alternatives: More streamlined than traditional email clients by automating the drafting process with templates.
This capability retrieves and synchronizes contact information to autofill recipient fields when composing emails. It integrates with the Gmail API's contacts endpoint to fetch user contacts and maintain an updated list, ensuring that the most relevant contacts are available for quick access. The implementation is designed to enhance user experience by reducing the time spent searching for contacts while composing emails.
Unique: Directly integrates with the Gmail API's contacts endpoint to provide real-time contact suggestions while composing emails.
vs alternatives: More efficient than manual entry methods by providing instant access to frequently used contacts.
This capability allows users to apply labels to emails for better organization and categorization. It utilizes the Gmail API's labeling features, enabling users to create, modify, and delete labels programmatically. The implementation is designed to help users manage their inboxes effectively by allowing them to categorize emails based on custom criteria, improving email retrieval and management.
Unique: Utilizes the Gmail API's built-in labeling system to allow for dynamic and programmatic organization of emails.
vs alternatives: More flexible than traditional email clients by allowing programmatic label management and organization.
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.
Gmail scores higher at 51/100 vs Supabase at 42/100.
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