| 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 8 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Converts any MCP tool into a paid endpoint using a lightweight Python or TypeScript decorator that intercepts tool invocations, validates payment credentials, and gates execution. The decorator pattern wraps the original tool function without modifying its signature, injecting payment validation logic at runtime before the tool executes. Supports multiple payment providers through a pluggable backend architecture.
Unique: Uses a two-line decorator syntax that preserves the original tool's function signature and behavior, allowing payment logic to be added without touching tool implementation code. This is achieved through Python/TypeScript decorator metaprogramming that wraps the tool function and intercepts calls at the MCP protocol level.
vs alternatives: Simpler than building custom MCP middleware or payment proxy layers because it operates at the function level rather than requiring protocol-level interception, reducing integration complexity for tool authors.
Provides a unified interface for integrating multiple payment backends (Stripe, custom HTTP endpoints, etc.) through a pluggable provider pattern. The abstraction decouples tool payment logic from specific payment provider implementations, allowing developers to swap providers or support multiple providers simultaneously without changing tool code. Implements provider-agnostic validation and error handling.
Unique: Implements a provider registry pattern where payment backends are registered at runtime, allowing tools to remain agnostic to the underlying payment system. Providers implement a common interface (validate_payment, get_user_balance, etc.) enabling hot-swapping without tool redeployment.
vs alternatives: More flexible than hardcoding Stripe-only logic because it treats payment providers as pluggable modules, enabling custom backends and multi-provider support without framework changes.
Manages authentication credentials and payment tokens for tool invocations, validating that incoming requests include valid payment authorization before tool execution. Implements credential extraction from MCP request context, token validation against payment provider, and credential caching to reduce provider API calls. Supports both API key and OAuth token patterns.
Unique: Integrates credential validation directly into the MCP tool invocation pipeline using decorator interception, extracting and validating credentials from MCP context without requiring explicit credential passing in tool parameters. Implements optional credential caching with configurable TTL to balance security and performance.
vs alternatives: More integrated than external API gateway approaches because it operates at the tool function level, allowing per-tool credential policies and reducing round-trips to external auth services.
Automatically captures payment-related events (authorization attempts, successes, failures, balance changes) and generates structured audit logs for compliance and debugging. Logs include timestamp, user ID, tool ID, payment status, provider response, and error details. Supports custom log handlers for integration with external logging systems (CloudWatch, Datadog, etc.).
Unique: Automatically logs all payment events at the decorator level without requiring explicit logging code in tools, capturing the full payment validation lifecycle (request, provider call, response, outcome) in structured format. Supports custom log handlers for flexible integration with any logging backend.
vs alternatives: More comprehensive than manual logging because it captures all payment events automatically at the framework level, ensuring no payment events are missed and providing consistent log format across all tools.
Enforces usage quotas and rate limits on paid tools based on user subscription tier or payment status, preventing abuse and ensuring fair resource allocation. Implements quota tracking (calls per minute/hour/day), tier-based limits (free tier: 10 calls/day, pro tier: 1000 calls/day), and quota reset scheduling. Integrates with payment provider to determine user tier and remaining quota.
Unique: Integrates quota enforcement directly into the payment decorator, checking both payment status and remaining quota before tool execution. Supports tier-based quota configuration where different subscription tiers have different limits, with quota state stored externally and checked on each invocation.
vs alternatives: More integrated than external rate limiting services because it combines payment status and quota enforcement in a single decorator, enabling tier-aware rate limiting without separate rate limit service.
Implements configurable error handling for payment provider failures, including retry strategies (exponential backoff, jitter), fallback behaviors (deny access, allow with deferred payment, etc.), and detailed error reporting. Distinguishes between transient failures (network timeout, provider temporarily unavailable) and permanent failures (invalid credentials, insufficient balance) to apply appropriate retry logic.
Unique: Implements provider-aware retry logic that distinguishes between transient and permanent payment failures, applying exponential backoff for transient failures while immediately failing permanent failures. Supports configurable fallback behaviors (deny, allow-deferred, etc.) to handle provider outages without blocking tool access.
vs alternatives: More sophisticated than simple retry-all approaches because it uses error code analysis to distinguish transient from permanent failures, avoiding wasted retries on permanent failures while ensuring resilience to temporary provider issues.
Provides identical decorator-based payment gating API in both Python and TypeScript, allowing developers to use the same patterns regardless of implementation language. Maintains feature parity between implementations (same decorator syntax, same provider abstraction, same configuration format) while using language-native patterns (Python decorators, TypeScript decorators). Shared documentation and examples work across both languages.
Unique: Maintains identical decorator-based API across Python and TypeScript implementations, using language-native decorator syntax (@paymcp.paid in Python, @paymcp.paid() in TypeScript) while preserving the same configuration and behavior. Shared provider abstraction allows tools to use the same payment backend regardless of language.
vs alternatives: More developer-friendly than language-specific payment libraries because developers can use the same patterns and mental models across Python and TypeScript projects, reducing cognitive load in polyglot environments.
Integrates directly with the MCP protocol layer to extract payment credentials and user context from MCP request metadata, without requiring explicit parameter passing in tool signatures. Implements MCP context parsing to retrieve user ID, API key, subscription tier, and other payment-relevant metadata from MCP request headers or custom context fields. Operates transparently to tool implementations.
Unique: Operates at the MCP protocol level to extract payment context from request metadata, allowing payment gating to work transparently without modifying tool function signatures or requiring tools to handle payment logic. Uses MCP context parsing to retrieve user ID, credentials, and subscription tier.
vs alternatives: More transparent than parameter-based approaches because it extracts payment context from MCP protocol metadata rather than requiring tools to accept payment parameters, keeping tool implementations clean and focused on business logic.
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 PayMCP at 23/100.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
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