| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
This capability allows users to perform complex searches within their Gmail inbox using various criteria such as sender, subject, date, or custom Gmail queries. It utilizes the Gmail API to execute search commands and retrieve filtered results, enabling users to quickly locate specific emails without manual browsing. The integration with Claude Desktop enhances the search experience by providing natural language processing for query interpretation.
Unique: Integrates directly with the Gmail API to leverage advanced search functionalities, providing a more powerful search experience than standard email clients.
vs alternatives: More efficient than traditional email clients because it utilizes direct API calls for real-time search results.
This capability automates the labeling of incoming emails based on predefined rules and patterns. It uses machine learning algorithms to analyze email content and metadata, allowing users to categorize emails automatically without manual intervention. The system can adapt to user behavior over time, improving its categorization accuracy and efficiency.
Unique: Employs machine learning to dynamically categorize emails, adapting to user preferences over time, unlike static rule-based systems.
vs alternatives: More adaptive than traditional email filters, which often require constant manual updates to remain effective.
This capability enables users to perform bulk actions on multiple emails simultaneously, such as deleting or archiving based on specific criteria. It uses batch processing through the Gmail API, allowing for efficient handling of large volumes of emails without the need for repetitive manual actions. Users can define criteria for bulk operations, streamlining their email management tasks.
Unique: Utilizes batch processing capabilities of the Gmail API to perform actions on multiple emails efficiently, reducing the time spent on email management.
vs alternatives: Faster than manual deletion methods in Gmail, which require individual actions for each email.
This capability provides users with insights into their email usage patterns, such as the volume of emails received, response times, and categorization trends. It aggregates data from the user's inbox and presents it in a user-friendly format, allowing for better understanding and management of email habits. The analytics are generated using data visualization techniques to highlight key metrics.
Unique: Combines email data aggregation with advanced visualization techniques to provide actionable insights, unlike basic email statistics available in standard clients.
vs alternatives: Offers deeper insights than typical email clients, which often provide only basic statistics without actionable analysis.
This capability automates the process of identifying and removing unwanted emails, such as old newsletters, notifications, and spam. It employs heuristics and user-defined criteria to determine which emails can be safely deleted or archived, ensuring that users maintain a clutter-free inbox. The integration with the Gmail API allows for efficient execution of these cleanup tasks.
Unique: Utilizes user-defined heuristics combined with Gmail API capabilities to automate email cleanup, providing a more tailored approach than generic spam filters.
vs alternatives: More customizable than standard email cleanup tools, which often lack user-defined criteria.
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 Manager 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