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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
This capability allows users to draft emails using context-aware suggestions by leveraging the Gmail API's ability to access previous threads and labels. It uses a combination of natural language processing and user behavior analysis to provide relevant suggestions, making the drafting process more efficient and personalized. The integration with the Gmail API ensures that drafts can be saved directly to the user's account, maintaining continuity.
Unique: Utilizes user-specific email context and historical data to provide tailored drafting suggestions, unlike generic drafting tools.
vs alternatives: More personalized than standard email drafting tools due to its integration with user email history.
This capability enables users to automatically retrieve emails based on specified labels using the Gmail API's filtering options. It employs a systematic approach to query the API for emails that match certain criteria, allowing for efficient organization and retrieval of messages. The use of labels allows for more granular control over which emails are fetched, enhancing user productivity.
Unique: Offers advanced filtering capabilities through Gmail labels, allowing for precise email retrieval compared to basic search functionalities.
vs alternatives: More efficient than standard email retrieval methods due to its label-based filtering approach.
This capability allows users to manage email threads by tracking conversation history through the Gmail API. It provides functions to retrieve, update, and organize threads, ensuring that users can maintain context in ongoing conversations. The architecture leverages the API's threading model, which groups related messages, making it easier to manage discussions without losing track of previous interactions.
Unique: Utilizes the Gmail API's built-in threading capabilities, allowing for seamless management of conversations without additional data processing.
vs alternatives: More effective than generic email management tools due to its direct integration with Gmail's threading model.
This capability allows users to automate the creation and management of labels in Gmail through the API. It supports batch operations for creating multiple labels at once and provides functions to update or delete existing labels. The implementation uses RESTful API calls to interact with Gmail's label management endpoints, streamlining the organization of emails based on user-defined criteria.
Unique: Enables batch processing of label management tasks, which is not commonly supported in standard email clients.
vs alternatives: More efficient than manual label management due to its automation capabilities.
This capability allows users to send emails using predefined templates, enhancing productivity by reducing repetitive tasks. It leverages the Gmail API's message sending functionality, allowing users to insert template content dynamically. The implementation includes a templating engine that can replace placeholders with user-specific data, ensuring that emails are personalized while maintaining a consistent format.
Unique: Integrates a templating engine for dynamic content insertion, providing a level of personalization not typically available in standard email clients.
vs alternatives: More flexible than traditional email clients due to its support for dynamic templates.
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 Gmail MCP at 29/100.
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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