| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 4 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Automatically generates strongly-typed TypeScript client code from GraphQL schemas using a proxy-based code generation approach. The tool introspects GraphQL schemas and emits type definitions that map GraphQL queries, mutations, and subscriptions to TypeScript interfaces, enabling compile-time type safety for GraphQL client operations without manual type annotation.
Unique: Uses a proxy-based code generation pattern specifically optimized for GraphQL clients, likely leveraging schema introspection with template-based type emission rather than AST manipulation, enabling lightweight integration into existing GraphQL toolchains
vs alternatives: Lighter-weight than full GraphQL code generators like GraphQL Code Generator by focusing specifically on type generation for proxy patterns, reducing configuration complexity for teams already using proxy-based GraphQL clients
Analyzes GraphQL client code (queries, mutations, subscriptions) and automatically infers corresponding TypeScript types by matching operations against the introspected schema. The tool uses pattern matching or AST analysis to identify GraphQL operations in client code and generates precise type definitions for operation variables and response shapes without manual annotation.
Unique: Specifically targets operation-level type inference using proxy patterns, likely analyzing GraphQL operation documents and correlating them with schema definitions to emit precise variable and response types without requiring separate type annotation files
vs alternatives: More focused than general-purpose GraphQL code generators by specializing in operation type inference for proxy-based clients, reducing the need for separate type definition files and enabling tighter integration with existing client code
Generates complete GraphQL proxy client implementations from schema definitions, creating wrapper functions or classes that encapsulate GraphQL operations with built-in type safety. The generator produces client code that handles query execution, variable binding, response parsing, and error handling while maintaining strict TypeScript type contracts derived from the schema.
Unique: Generates complete proxy client implementations rather than just types, using schema introspection to emit functional client code with built-in operation handling, variable binding, and response type mapping in a single generation pass
vs alternatives: More comprehensive than type-only generators by producing executable client code alongside types, reducing the gap between schema definition and usable client implementation compared to tools that only emit type definitions
Validates GraphQL schemas and generated client code at build time, checking for type mismatches, missing operations, and schema inconsistencies before runtime. The tool integrates with TypeScript compilation and build pipelines to catch schema-related errors during development, preventing invalid GraphQL operations from reaching production.
Unique: Integrates schema validation directly into the build pipeline using proxy pattern awareness, likely hooking into TypeScript compilation or webpack loaders to validate generated client code against schema definitions without requiring separate validation steps
vs alternatives: Tighter integration with build systems than standalone GraphQL validators, catching schema violations as part of normal TypeScript compilation rather than requiring separate validation commands or CI steps
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 asma-genql-proxy at 21/100. asma-genql-proxy leads on ecosystem, while Supabase is stronger on adoption and 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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