Supabase ranks higher at 42/100 vs mcp-x402-stripe-privy-solana_agent at 25/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | mcp-x402-stripe-privy-solana_agent | Supabase |
|---|---|---|
| Type | MCP Server | MCP Server |
| UnfragileRank | 25/100 | 42/100 |
| Adoption | 0 | 0 |
| Quality |
| 0 |
| 0 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 7 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Exposes Stripe payment APIs through the Model Context Protocol, allowing LLM agents to initiate charges, create payment intents, and manage customer billing without direct API access. Implements MCP tool schema bindings that translate natural language agent requests into authenticated Stripe API calls with proper error handling and transaction state management.
Unique: Bridges Stripe's REST API into MCP's tool-calling schema, enabling LLM agents to execute payment operations through natural language without implementing custom Stripe SDK bindings — uses MCP's standardized function registry to abstract payment complexity
vs alternatives: Simpler than building custom Stripe integrations for each agent framework; more secure than exposing Stripe keys directly to LLMs because credentials stay server-side in the MCP process
Integrates Privy's embedded wallet and authentication system through MCP, allowing agents to verify user identity, manage wallet connections, and access user credentials without direct blockchain interaction. Implements Privy's OAuth-like flow adapted for MCP's request/response model, with support for multi-chain wallet abstraction (Solana, Ethereum, etc.).
Unique: Wraps Privy's embedded wallet abstraction as MCP tools, allowing agents to query authenticated user wallets without managing private keys or blockchain RPC calls — delegates credential security to Privy's custody model
vs alternatives: More secure than agents directly managing private keys; simpler than building custom wallet abstraction layers because Privy handles multi-chain normalization and user UX
Provides MCP tools for querying Solana blockchain state, building unsigned transactions, and preparing transaction payloads for signing. Uses Solana Web3.js library to construct transaction instructions, manage program interactions, and serialize transactions into formats compatible with Privy or other signers. Handles RPC communication with Solana validators through configurable endpoints.
Unique: Exposes Solana transaction building as MCP tools rather than requiring agents to import Web3.js directly, abstracting RPC communication and transaction serialization — allows agents to compose complex multi-instruction transactions through natural language
vs alternatives: Safer than agents directly calling Solana RPC because MCP server validates transaction structure before returning; more flexible than pre-built transaction templates because agents can compose arbitrary instruction sequences
Aggregates balance and account state queries across Solana and other blockchains (via Privy's wallet abstraction), returning normalized wallet data through a single MCP interface. Queries multiple RPC endpoints in parallel and caches results to reduce latency, with fallback logic if primary endpoints are unavailable.
Unique: Normalizes balance queries across heterogeneous blockchains (Solana, EVM chains) into a single MCP tool interface with parallel RPC calls and caching, eliminating the need for agents to manage chain-specific RPC logic
vs alternatives: More efficient than sequential RPC calls because parallel queries reduce latency by 60-70%; more reliable than single-endpoint queries because fallback logic handles RPC downtime
Coordinates multi-step workflows combining Stripe payments, Solana transactions, and Privy authentication through MCP's tool-calling interface. Implements state machine logic that sequences operations (authenticate user → check balance → initiate payment → build blockchain transaction → sign → execute), with error recovery and transaction rollback capabilities.
Unique: Chains Stripe, Solana, and Privy tools into coordinated workflows through MCP's sequential tool calling, with state tracking across payment and blockchain steps — enables agents to reason about multi-rail transactions without custom orchestration code
vs alternatives: More flexible than pre-built payment APIs because agents can compose arbitrary sequences; more reliable than manual API chaining because MCP handles error propagation and state consistency
Implements structured error responses for failed payments, blockchain transactions, and authentication errors, with recovery suggestions that agents can act on. Maps Stripe error codes, Solana RPC errors, and Privy auth failures to actionable recovery steps (retry with backoff, use alternative payment method, re-authenticate), enabling agents to autonomously recover from transient failures.
Unique: Translates domain-specific errors (Stripe, Solana, Privy) into agent-actionable recovery suggestions through MCP, allowing agents to autonomously decide retry vs. escalation — implements error classification and recovery routing without requiring agents to parse raw API errors
vs alternatives: More intelligent than generic retry logic because it understands error semantics; more autonomous than requiring human intervention because agents can execute recovery suggestions
Implements the Model Context Protocol specification for tool definition, request/response serialization, and error handling. Defines JSON schemas for each capability (Stripe payments, Solana transactions, Privy auth) that conform to MCP's tool-calling standard, enabling compatibility with any MCP-compliant client (Claude, custom LLM runners, etc.). Handles MCP's request/response lifecycle including parameter validation and type coercion.
Unique: Implements MCP as the primary integration layer rather than exposing raw APIs, ensuring compatibility with any MCP-compliant LLM client — uses MCP's standardized tool schema to abstract implementation details from clients
vs alternatives: More portable than custom API integrations because MCP is an open standard; more discoverable than REST APIs because MCP supports tool introspection and documentation
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 mcp-x402-stripe-privy-solana_agent at 25/100. mcp-x402-stripe-privy-solana_agent leads on ecosystem, while Supabase is stronger on quality.
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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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