| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Exposes Val Town's native tools and utilities as Model Context Protocol (MCP) resources, enabling Claude and other MCP-compatible clients to discover and invoke Val Town functions through standardized MCP resource/tool schemas. The server implements the MCP specification to translate between Val Town's execution environment and the MCP protocol's request/response model, allowing seamless integration of Val Town capabilities into LLM agent workflows without custom API wrappers.
Unique: Implements MCP server protocol specifically for Val Town's execution model, translating Val Town's function-as-a-service paradigm into MCP's standardized tool/resource abstraction rather than wrapping Val Town as a generic HTTP API
vs alternatives: Provides native MCP integration for Val Town without requiring custom HTTP wrapper layers, enabling Claude and other MCP clients to treat Val Town functions as first-class tools with proper schema discovery and error handling
Implements the full Model Context Protocol server specification, handling MCP message parsing, request routing, capability negotiation, and lifecycle events (initialization, shutdown). The server manages bidirectional communication with MCP clients, implements the MCP transport layer (stdio or HTTP), and handles protocol versioning and feature negotiation to ensure compatibility across different MCP client implementations.
Unique: Provides a ready-to-use MCP server scaffold specifically tailored for Val Town integration, abstracting away MCP protocol boilerplate so developers focus on tool bridging rather than protocol compliance
vs alternatives: Eliminates the need to manually implement MCP protocol handling from scratch, reducing integration time compared to building a custom MCP server or using generic HTTP-to-MCP adapters
Automatically discovers available Val Town functions and extracts their signatures, parameter schemas, return types, and documentation to expose as MCP tool definitions. The server queries Val Town's API or introspection endpoints to build a dynamic tool catalog, generating JSON schemas for function parameters that MCP clients can use for validation and UI generation, without requiring manual tool definition files.
Unique: Implements dynamic schema extraction from Val Town's function metadata rather than requiring static tool definition files, enabling the tool catalog to stay in sync with Val Town changes automatically
vs alternatives: Avoids manual tool definition maintenance compared to static MCP server configurations, reducing drift between Val Town functions and exposed MCP tools
Executes Val Town functions through the MCP protocol by marshaling parameters from MCP tool call requests into Val Town's execution format, invoking the function, and returning results back through the MCP response channel. Handles parameter type conversion, error propagation, timeout management, and result serialization to ensure Val Town execution semantics are preserved across the MCP boundary.
Unique: Implements transparent parameter marshaling between MCP's JSON-RPC format and Val Town's function execution model, handling type conversion and error propagation without requiring developers to write custom adapters
vs alternatives: Provides seamless function invocation compared to manual HTTP API calls, with proper error handling and parameter validation built into the MCP protocol layer
Abstracts the MCP transport layer (stdio, HTTP, WebSocket) to support multiple MCP client implementations (Claude desktop, custom agents, LLM frameworks). The server negotiates protocol features during initialization and adapts its responses based on client capabilities, ensuring compatibility across different MCP client versions and implementations without requiring code changes.
Unique: Implements transport-agnostic MCP server that works with Claude desktop (stdio), HTTP clients, and custom agents without requiring separate server instances or client-specific code paths
vs alternatives: Provides broader client compatibility than single-transport MCP servers, enabling deployment to both local (Claude desktop) and remote (cloud agents) environments with one codebase
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 valjs-mcp-alpha at 20/100.
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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