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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 9 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Implements the Model Context Protocol (MCP) specification via the YepCodeMcpServer class in src/server.ts, acting as a bridge that translates YepCode's cloud capabilities into standardized MCP tools consumable by AI platforms. The server maintains strict type safety through Zod schema validation and routes incoming MCP requests to appropriate tool handlers organized into five distinct categories: storage, environment variables, code execution, process execution, and dynamically discovered processes. This enables AI assistants like Claude Desktop or Cursor IDE to invoke YepCode operations through a unified, protocol-compliant interface.
Unique: Implements full MCP protocol compliance with Zod-based schema validation for all tool inputs, providing strict type safety and automatic request validation before execution. The YepCodeMcpServer class orchestrates both static tool definitions (storage, environment, code execution) and dynamically discovered tools from tagged YepCode processes, enabling AI systems to discover and invoke both built-in and custom capabilities.
vs alternatives: More comprehensive than basic API wrappers because it implements the full MCP specification with schema validation, enabling seamless integration with multiple AI platforms through a single standardized interface rather than requiring platform-specific adapters.
Exposes YepCode's cloud-based sandbox runtime through the run-code-tool-definitions.ts module, allowing AI systems to execute arbitrary JavaScript or Python code in an isolated, secure environment. The implementation leverages the @yepcode/run package to handle runtime isolation, package management (NPM and PyPI), and execution lifecycle. Code execution requests are validated through Zod schemas before being dispatched to YepCode's infrastructure, which manages resource limits, timeout enforcement, and output capture. This enables AI agents to execute generated code without exposing the host system to security risks.
Unique: Provides true sandboxed execution through YepCode's cloud infrastructure rather than in-process evaluation, eliminating security risks from executing untrusted code. Supports both JavaScript and Python with full NPM and PyPI package ecosystem access, validated through Zod schemas before dispatch to the runtime.
vs alternatives: Safer than eval() or vm2 because execution happens in isolated cloud infrastructure with enforced resource limits, and more flexible than simple REST APIs because it integrates directly into MCP tool workflows with automatic schema validation.
Implements file operations (create, read, update, delete, list) through the storage-tool-definitions.ts module, exposing YepCode's file storage as MCP tools. Each storage operation is validated through Zod schemas and routed through the YepCodeMcpServer to YepCode's backend storage service. This allows AI systems to persist data, manage configuration files, and organize code artifacts within the YepCode workspace without requiring direct filesystem access. The tool definitions include metadata about supported operations and parameter constraints.
Unique: Exposes YepCode's cloud storage as MCP tools rather than requiring direct filesystem access, providing workspace-scoped isolation and automatic schema validation for all file operations. The storage-tool-definitions.ts module generates tool definitions with parameter constraints that prevent invalid operations at the MCP layer.
vs alternatives: More secure than direct filesystem access because operations are scoped to YepCode workspace and validated through Zod schemas, and more integrated than separate storage APIs because it's exposed as native MCP tools discoverable by AI systems.
Manages workspace environment variables through the env-vars-tool-definitions.ts module, allowing AI systems to read, set, and delete environment variables within the YepCode workspace scope. Variables are stored in YepCode's backend and validated through Zod schemas before being applied. This enables AI agents to configure runtime behavior, manage secrets (with appropriate security considerations), and pass data between code execution invocations without exposing variables to the host system. The implementation maintains strict workspace isolation — variables are scoped to the authenticated workspace only.
Unique: Provides workspace-scoped environment variable management through MCP tools with Zod schema validation, ensuring variables are isolated to the authenticated workspace and validated before storage. The implementation maintains separation between variable names (visible) and values (accessible only through authenticated requests).
vs alternatives: More secure than passing secrets through code parameters because variables are stored server-side and scoped to workspace, and more flexible than static configuration because AI agents can dynamically modify environment state during execution.
Automatically discovers and generates MCP tool definitions from tagged YepCode processes at runtime through the get-execution-tool-definition.ts module. The YepCodeMcpServer scans the authenticated workspace for processes marked with specific tags and dynamically creates tool definitions that expose those processes as invocable MCP tools. Each generated tool includes parameter schemas derived from the process definition, enabling AI systems to discover and invoke custom YepCode processes without requiring manual tool registration. This pattern allows users to extend YepCode capabilities by creating processes that are automatically exposed to AI systems.
Unique: Implements runtime process discovery and automatic MCP tool generation, allowing users to extend YepCode capabilities by creating processes that are automatically exposed to AI systems without requiring code changes to the MCP server. The get-execution-tool-definition.ts module generates tool schemas dynamically from process definitions.
vs alternatives: More extensible than static tool lists because new processes become available automatically, and more user-friendly than manual tool registration because process creators don't need to understand MCP protocol details.
Supports multiple deployment patterns through configuration options in README.md and package.json entry points, enabling the MCP server to run as a local Node.js process, remote HTTP service, or containerized Docker deployment. The server can be configured via environment variables (YEPCODE_API_TOKEN, YEPCODE_MCP_OPTIONS) and URL query parameters for remote deployments. This flexibility allows teams to integrate YepCode into different AI platform architectures — Claude Desktop uses local stdio transport, while custom platforms may use HTTP or other transport mechanisms. The implementation maintains consistent tool behavior across all deployment models.
Unique: Provides three distinct deployment models (local, remote, Docker) with unified configuration through environment variables and URL parameters, allowing the same MCP server codebase to operate in different architectural contexts without modification. The package.json defines multiple entry points for different deployment scenarios.
vs alternatives: More flexible than single-deployment solutions because it supports local (Claude Desktop), remote (custom platforms), and containerized (cloud) deployments from the same codebase, reducing maintenance burden compared to maintaining separate implementations.
Enforces strict type safety across all MCP tool invocations through Zod schema validation in src/types.ts and individual tool definition files. Every incoming MCP request is validated against its corresponding Zod schema before being dispatched to YepCode infrastructure, preventing malformed requests from reaching the backend. The type system is defined in TypeScript with Zod runtime validation, providing both compile-time type checking and runtime safety. This approach catches invalid inputs early and provides clear error messages to AI systems when requests don't match expected schemas.
Unique: Implements comprehensive Zod-based schema validation for all MCP tool inputs, providing both compile-time TypeScript type checking and runtime validation. The src/types.ts module defines request/response types with Zod schemas that are reused across all tool definitions.
vs alternatives: More robust than optional validation because all inputs are validated before execution, and more maintainable than manual validation because Zod schemas serve as both runtime validators and type definitions.
Implements structured error handling throughout the MCP server that returns MCP-compliant error codes and messages when tool invocations fail. The error handling strategy is defined in src/server.ts and applied consistently across all tool categories. Errors from YepCode backend operations are caught, transformed into MCP error responses with appropriate error codes, and returned to the AI system with context about what failed. This enables AI systems to understand and potentially recover from errors rather than receiving opaque failure messages.
Unique: Implements MCP-compliant error handling that transforms YepCode backend errors into structured MCP error responses with appropriate error codes, enabling AI systems to understand and respond to failures programmatically rather than treating all errors as opaque failures.
vs alternatives: More useful than generic error messages because it provides MCP-compliant error codes that AI systems can interpret, and more debuggable than silent failures because it includes context about what went wrong.
+1 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 YepCode at 25/100.
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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