| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
This capability allows users to set spending limits for AI agents on-chain using a Solana program. The program enforces constraints such as per-transaction, daily, and lifetime limits, ensuring that even if the agent is compromised, it cannot exceed these limits. This is achieved through smart contracts that automatically validate transactions against the defined constraints before execution.
Unique: Utilizes Solana's blockchain to enforce spending limits at the protocol level, ensuring high security and immutability of constraints.
vs alternatives: More secure than traditional wallet management solutions as it enforces limits directly on-chain, reducing the risk of agent misuse.
This capability provides users with the ability to monitor the health and status of their AI agents in real-time. It aggregates data on constraints, spending history, and overall agent performance, presenting it through a user-friendly dashboard. The implementation leverages WebSocket connections for live updates, ensuring users receive immediate feedback on their agent's activity.
Unique: Integrates WebSocket technology for real-time updates, providing immediate insights into agent performance and constraints.
vs alternatives: Offers more immediate feedback compared to polling-based solutions, enhancing user responsiveness to agent activities.
This capability allows agents to autonomously process payments using the x402 protocol, which is designed for pay-per-use APIs. The implementation involves a secure transaction mechanism that checks the agent's spending limits before executing payments, ensuring compliance with user-defined constraints. This is facilitated through a series of API calls that interact with the blockchain to confirm payment eligibility.
Unique: Employs the x402 protocol specifically for autonomous payment processing, ensuring that all transactions are compliant with pre-set spending limits.
vs alternatives: More streamlined than traditional payment systems, as it allows for seamless integration with autonomous agents without manual payment handling.
This capability enables users to check the balances of their AI agents in SOL and USDC while also displaying the enforced spending limits. It utilizes a secure API call to the blockchain to fetch the current balance and constraints, ensuring that users are always aware of their agent's financial status and limitations. The implementation ensures that balance checks are performed in real-time, providing accurate and up-to-date information.
Unique: Combines balance checking with real-time constraint visibility, allowing users to manage their agents' finances more effectively.
vs alternatives: More integrated than standalone balance checkers, as it directly ties spending limits to the financial overview.
This capability allows users to instantly freeze their AI agents in case of suspicious activity or when limits are reached. It utilizes a simple API call to the blockchain to trigger a freeze, which is enforced by the smart contract. This ensures that the agent cannot perform any transactions until it is manually unfrozen, providing an additional layer of security.
Unique: Offers an immediate response mechanism to halt agent operations, leveraging smart contract capabilities for enforcement.
vs alternatives: Faster than traditional methods of stopping transactions, as it directly interacts with the blockchain for instant effect.
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 Cloaked Agent at 28/100.
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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