| 0 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
This capability enables users to search for and register AI agents across multiple protocols such as MCP, A2A, and agents.txt. It employs a unified search algorithm that indexes over 18,000 MCP servers from 6+ registries, allowing for efficient retrieval of agent information. The architecture is designed to be protocol-agnostic, ensuring compatibility across various agent ecosystems, which is a significant advantage in a fragmented landscape.
Unique: Utilizes a centralized indexing system that aggregates data from multiple registries, allowing for real-time updates and searches across diverse protocols.
vs alternatives: More comprehensive than single-protocol solutions as it consolidates agent information from multiple sources into one searchable interface.
This capability provides a built-in validator and linter for agents.txt files, ensuring that users can create compliant and error-free agent declarations. It works by parsing the agents.txt structure and checking against predefined rules and standards, offering feedback on potential issues in real-time. This feature is particularly useful for developers looking to maintain high-quality agent metadata.
Unique: Incorporates real-time linting capabilities that provide immediate feedback, unlike traditional validators that only check files post-creation.
vs alternatives: Faster and more interactive than static validation tools, allowing for iterative development of agents.txt files.
This capability allows users to register their AI agents with various MCP servers seamlessly. It uses a RESTful API to communicate with the servers, sending agent metadata and receiving confirmation of registration. The implementation includes error handling to manage server responses and ensure that users are informed of the registration status, which is crucial for maintaining an updated agent directory.
Unique: Features a robust error handling mechanism that provides detailed feedback on registration failures, enhancing the user experience.
vs alternatives: More reliable than basic registration tools due to its comprehensive error management and support for multiple server types.
This capability enables the platform to work seamlessly across different agent protocols, such as MCP, A2A, and agents.txt. It employs an abstraction layer that standardizes interactions with various protocols, allowing developers to integrate agents without worrying about the underlying protocol specifics. This design choice promotes flexibility and reduces the complexity of managing multiple integrations.
Unique: Utilizes an abstraction layer that allows for seamless integration across multiple protocols, reducing the need for protocol-specific code.
vs alternatives: More versatile than protocol-specific tools, enabling developers to adapt to changes in the agent ecosystem without significant rework.
This capability allows users to perform real-time searches of the agent directory, leveraging a fast indexing engine that retrieves results based on user queries. The search functionality is optimized for speed and accuracy, providing relevant results from a vast database of over 18,000 agents. The implementation includes features like autocomplete and filtering to enhance the search experience.
Unique: Incorporates a fast indexing engine that supports real-time updates and searches, ensuring that users always access the most current agent information.
vs alternatives: Faster and more responsive than traditional directory search tools due to its real-time indexing 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 Global Chat MCP Server 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