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| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 14 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Deploys a Model Context Protocol server on Cloudflare Workers, providing a globally distributed, edge-compute endpoint for AI assistants. The system uses Cloudflare's KV storage for state management and integrates with external OAuth and Stripe services via HTTP APIs. Requests flow through a central /sse endpoint that handles Server-Sent Events for real-time tool execution and response streaming.
Unique: Uses Cloudflare Workers as the execution environment instead of traditional Node.js servers or Lambda, providing edge-location execution and automatic global distribution without explicit multi-region configuration. Integrates Cloudflare KV for state storage, eliminating the need for external databases for authentication tokens and user sessions.
vs alternatives: Faster global latency and simpler deployment than AWS Lambda-based MCP servers, with built-in edge caching and no cold-start penalties compared to traditional containerized approaches.
Implements a dual-provider OAuth authentication system using the OAuthProvider class that verifies user identity through Google or GitHub. Authentication tokens are stored in Cloudflare KV storage (OAUTH_KV) and validated on each request. The system handles the OAuth redirect flow, token exchange, and session management without requiring users to create new credentials.
Unique: Implements OAuth token storage directly in Cloudflare KV rather than requiring an external database, reducing infrastructure dependencies. The OAuthProvider class abstracts both Google and GitHub flows behind a unified interface, allowing developers to switch providers or support both simultaneously without changing tool code.
vs alternatives: Simpler than Auth0 or Firebase Auth for MCP-specific use cases, with no monthly costs or vendor lock-in; faster than traditional session-based auth because tokens are validated against edge-local KV storage rather than making round-trips to a central auth server.
Validates tool inputs against JSON Schema definitions before execution, ensuring that only well-formed requests reach tool handlers. The system compares incoming tool parameters against the tool's declared inputSchema, rejects invalid inputs with detailed error messages, and prevents malformed requests from causing tool failures. Validation happens automatically as part of the tool execution pipeline.
Unique: Integrates JSON Schema validation directly into the tool execution pipeline, validating inputs before they reach tool handlers. This is automatic and transparent to tool developers — they declare a schema and validation happens without custom code.
vs alternatives: More robust than ad-hoc validation because it uses a standard schema format; faster than runtime type checking because validation happens once at invocation time; clearer error messages than generic type errors because JSON Schema provides detailed validation failure reasons.
Implements the Model Context Protocol (MCP) specification, allowing AI assistants to discover available tools, inspect their schemas, and invoke them dynamically. The system exposes tool metadata (name, description, input schema) via MCP protocol messages, handles tool invocation requests, and returns results in MCP-compliant format. This enables seamless integration with MCP-compatible clients like Claude and Cursor.
Unique: Implements the full MCP protocol stack, handling tool discovery, schema validation, and invocation orchestration. This allows AI assistants to dynamically discover and invoke tools without pre-configuration, enabling a more flexible integration model than traditional API-based approaches.
vs alternatives: More flexible than hardcoded tool integrations because AI assistants can discover tools dynamically; more standardized than custom APIs because it uses the MCP specification; better for multi-assistant support because a single MCP server works with any MCP-compatible client.
Orchestrates the complete request lifecycle from initial connection through authentication, payment validation, tool execution, and response streaming. The system validates OAuth tokens, checks payment status (if applicable), validates tool inputs, executes the tool handler, and streams results via SSE. Each step is enforced in sequence — requests fail fast if authentication or payment checks fail, preventing unnecessary tool execution.
Unique: Implements a sequential request pipeline where authentication, payment, and validation are enforced in order before tool execution. This is distinct from middleware-based approaches because the entire flow is integrated into the tool execution framework, providing tight coupling between access control and tool invocation.
vs alternatives: More secure than separate authentication and payment layers because access control is enforced at the point of tool execution; simpler than custom middleware because the pipeline is built into the framework; faster than external API calls because validation happens locally in the Worker.
Provides structured error handling throughout the request lifecycle, returning detailed error messages for authentication failures, payment validation failures, input validation errors, and tool execution errors. Errors are formatted as JSON responses or SSE messages, allowing AI assistants to understand what went wrong and potentially retry or adjust their requests. Error messages include context (which step failed, why) without leaking sensitive information.
Unique: Integrates error handling throughout the request pipeline, providing context-specific error messages at each stage (authentication, payment, validation, execution). Errors are formatted consistently as JSON or SSE messages, allowing AI assistants to parse and respond to failures programmatically.
vs alternatives: More informative than generic 500 errors because it provides context about which step failed; more secure than raw exception messages because sensitive details are filtered; better for AI assistant integration because structured error messages enable programmatic error handling.
Integrates Stripe payment processing through the PaidMcpAgent class, supporting three distinct payment models: subscription-based (recurring charges), metered usage (pay-per-use), and one-time payments. Before a user accesses a paid tool, the system checks their payment status via Stripe API; unpaid users receive a checkout URL. Payment history and subscription status are tracked and validated on each tool invocation.
Unique: Implements payment gating directly within the MCP tool execution flow via PaidMcpAgent, checking payment status before tool invocation rather than at the API gateway level. Supports three distinct payment models (subscription, metered, one-time) within a single framework, allowing developers to mix payment types across different tools without separate implementations.
vs alternatives: More flexible than simple API key-based access control because it enables recurring revenue and usage-based pricing; tighter integration than external payment gateways because payment checks happen synchronously during tool execution, preventing unpaid access.
Provides a declarative tool registration system where developers define tools (free or paid) with metadata including name, description, input schema, and payment model. The BoilerplateMCP class (extending PaidMcpAgent) manages tool registration, validates input against schemas, executes tool handlers, and enforces payment requirements. Tools are exposed via the MCP protocol, allowing AI assistants to discover and invoke them dynamically.
Unique: Implements tool registration as a declarative pattern where developers pass tool metadata and handlers to a registration method, which automatically exposes them via MCP protocol. The framework handles payment gating, input validation, and execution orchestration transparently, allowing developers to focus on tool logic rather than protocol details.
vs alternatives: Simpler than building custom MCP servers from scratch because it provides the boilerplate for authentication, payment, and protocol handling; more flexible than hardcoded tool lists because tools are registered dynamically at runtime.
+6 more 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 mcp-boilerplate 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