| 0 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 7 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Wraps functional-models ORM instances as Model Context Protocol (MCP) servers, allowing LLM clients to interact with database models through standardized MCP resource and tool interfaces. Implements the MCP server specification to translate ORM operations into protocol-compliant request/response handlers, enabling frontend applications and AI agents to query and manipulate data without direct database access.
Unique: Bridges functional-models ORM directly to MCP protocol without intermediate REST layer, using MCP's native resource and tool abstractions to expose model CRUD operations. Leverages functional-models' declarative model system to auto-generate MCP tool schemas from model definitions.
vs alternatives: Simpler than building a custom REST API + MCP client wrapper because it directly implements MCP server semantics; more type-safe than generic database MCP providers because it uses functional-models' model-aware validation and relationships.
Automatically maps functional-models ORM model definitions (entities, fields, relationships) to MCP resource endpoints, allowing LLM clients to discover and fetch model instances as structured resources. Uses reflection or schema introspection on functional-models models to generate MCP resource URIs and content types, enabling semantic understanding of data structure without manual configuration.
Unique: Uses functional-models' declarative model system as the source of truth for MCP resource schemas, eliminating manual schema duplication. Introspects model metadata at server initialization to generate resource endpoints dynamically.
vs alternatives: More maintainable than hand-written MCP resource handlers because schema changes in functional-models automatically propagate to MCP; more discoverable than REST APIs because MCP clients can enumerate resources and understand relationships natively.
Exposes functional-models ORM CRUD operations (create, read, update, delete, query) as MCP tools with schema-validated parameters. Translates MCP tool call requests into functional-models method invocations, handles validation errors, and returns results in MCP tool result format. Implements parameter marshaling to convert JSON tool arguments into ORM-compatible types (e.g., nested objects for relationships).
Unique: Generates MCP tool schemas directly from functional-models model definitions, ensuring tool parameters always match ORM expectations. Implements parameter marshaling to handle nested relationships and type conversions transparently.
vs alternatives: More type-safe than generic database MCP tools because it validates against functional-models schemas; more efficient than REST-based approaches because it avoids HTTP serialization overhead and can batch operations within a single MCP call.
Provides server initialization, connection handling, and lifecycle hooks optimized for frontend environments (browser or Electron). Implements MCP server protocol with support for stdio, WebSocket, or Server-Sent Events (SSE) transports, allowing frontend applications to spawn and communicate with the ORM datastore provider without a separate backend process. Handles graceful shutdown, error recovery, and connection state management.
Unique: Optimizes MCP server lifecycle for frontend environments by supporting stdio transport (for in-process communication) and providing connection pooling/reconnection logic. Abstracts transport complexity so frontend developers can treat the ORM as a local service.
vs alternatives: Simpler than deploying a separate backend MCP server because it runs embedded in the frontend process; more reliable than REST APIs for frontend use because it avoids CORS issues and provides native protocol-level error handling.
Translates MCP tool call filter parameters (JSON objects) into functional-models query syntax, executes filtered queries against the ORM, and returns paginated or limited result sets. Supports common filter operators (equals, contains, range, logical AND/OR) and translates them to functional-models filter API calls. Implements result pagination to prevent memory exhaustion from large queries.
Unique: Translates MCP tool filter parameters directly to functional-models query API, avoiding intermediate query language parsing. Implements pagination at the ORM level to prevent memory exhaustion and provide streaming-friendly result handling.
vs alternatives: More efficient than SQL-based query builders because it uses ORM-native query methods; safer than exposing raw SQL because it prevents injection attacks and enforces functional-models validation rules.
Handles functional-models relationship definitions (one-to-many, many-to-many, foreign keys) and exposes them through MCP resources and tools. When an LLM requests a model instance, automatically loads or provides access to related records. Implements lazy loading or eager loading strategies to balance performance and data completeness, preventing N+1 query problems through relationship batching.
Unique: Leverages functional-models relationship metadata to automatically generate MCP resources for related records, avoiding manual relationship exposure. Implements relationship batching to prevent N+1 queries when LLMs traverse multiple relationships.
vs alternatives: More efficient than exposing relationships as separate tool calls because it batches relationship loading; more maintainable than REST APIs with custom relationship endpoints because relationship definitions are centralized in functional-models models.
Captures functional-models validation errors, ORM operation failures, and database errors, translating them into MCP-compatible error responses with actionable feedback for LLM clients. Implements error categorization (validation, constraint violation, not found, permission denied) and provides structured error messages that LLMs can parse and act upon. Prevents sensitive database error details from leaking to clients.
Unique: Translates functional-models validation errors into MCP error format with field-level feedback, enabling LLMs to understand and correct invalid operations. Sanitizes database errors to prevent information leakage while preserving actionable details.
vs alternatives: More informative than generic HTTP error codes because it provides structured validation feedback; more secure than exposing raw database errors because it sanitizes sensitive information while preserving LLM-actionable details.
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 functional-models-orm-mcp at 26/100.
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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