| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
This capability allows users to query balances across multiple blockchain networks including NERVE, NULS, ETH, BSC, and TRON. It utilizes a unified JSON-RPC interface that abstracts the underlying differences between these chains, enabling seamless interaction. The implementation leverages a microservices architecture to handle requests concurrently, improving response times for balance inquiries across chains.
Unique: Uses a microservices architecture to handle requests concurrently, significantly improving performance compared to traditional RPC servers.
vs alternatives: More efficient than standard RPC servers due to its concurrent request handling.
This capability enables users to query transaction details across multiple blockchain networks. It employs a standardized JSON-RPC interface to retrieve transaction data, which is then formatted uniformly regardless of the underlying blockchain. The architecture supports caching mechanisms to enhance performance for frequently accessed transaction data.
Unique: Integrates caching mechanisms to speed up transaction data retrieval, which is often a bottleneck in traditional systems.
vs alternatives: Faster transaction lookups than standard RPC servers due to built-in caching.
This capability allows users to retrieve block data from various blockchain networks through a unified interface. It employs a JSON-RPC API that abstracts the specifics of each blockchain's block structure, allowing for easy access to block height, timestamp, and transaction count. The implementation uses asynchronous calls to ensure that block data is fetched without blocking other operations.
Unique: Utilizes asynchronous calls to fetch block data, allowing for non-blocking operations that improve overall system responsiveness.
vs alternatives: More responsive than traditional RPC servers due to its asynchronous architecture.
This capability provides integration with the NerveSwap decentralized exchange APIs, allowing users to access swap data and perform trades programmatically. It utilizes a RESTful interface that simplifies interaction with the DEX, abstracting the complexities of smart contract interactions. The implementation includes built-in error handling and response validation to ensure robust trading operations.
Unique: Provides a simplified RESTful interface for interacting with NerveSwap, reducing the complexity of smart contract interactions.
vs alternatives: Easier to use than direct smart contract calls, which require deeper blockchain knowledge.
This capability allows users to query information about consensus nodes across multiple blockchain networks. It uses a unified JSON-RPC interface to retrieve details such as node status, voting power, and participation rates. The architecture supports real-time updates, ensuring that users receive the most current data about consensus nodes.
Unique: Enables real-time updates for consensus node data, which is often static in traditional querying systems.
vs alternatives: More dynamic than traditional RPC servers, which may provide stale data.
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 Nerve RPC 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
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