| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Exposes Regle form validation logic as an MCP (Model Context Protocol) server, allowing LLM clients to invoke validation rules and schema definitions through standardized MCP resource and tool endpoints. The server translates Regle's Vue-based validation framework into language-agnostic MCP protocol messages, enabling AI models to understand and apply form validation constraints without direct Vue dependency.
Unique: Bridges Vue-based form validation (Regle) with MCP protocol, allowing LLMs to natively understand and apply form constraints without reimplementing validation logic. Uses MCP's resource and tool abstractions to expose Regle's declarative validation rules as composable AI capabilities.
vs alternatives: Enables AI agents to validate forms using existing Regle schemas via MCP, avoiding duplication of validation logic compared to manually describing rules to LLMs or building custom validation endpoints.
Registers Regle validation rules as callable MCP tools, allowing LLM clients to invoke specific validators (required, email, minLength, custom rules) with typed parameters. The server introspects Regle schema definitions and generates MCP tool schemas that describe each validator's signature, constraints, and error messages, enabling AI models to understand which validators apply to which form fields.
Unique: Automatically generates MCP tool schemas from Regle validator definitions, allowing LLMs to discover and invoke validators with proper type hints and constraints without manual tool registration. Uses introspection to keep tool definitions in sync with Regle schema changes.
vs alternatives: More maintainable than manually defining validation tools for each field type — schema changes automatically propagate to LLM tool definitions, whereas custom REST endpoints require manual updates.
Publishes Regle form schemas as MCP resources, allowing LLM clients to read and understand the complete form structure, field definitions, validation rules, and metadata through the MCP resource protocol. The server exposes schemas as queryable resources that clients can fetch to build context about form requirements before processing user input.
Unique: Exposes Regle schemas as MCP resources rather than embedding them in tool descriptions, allowing LLMs to fetch schema details on-demand and maintain a persistent understanding of form structure across multiple validation calls. Separates schema knowledge from validator tools.
vs alternatives: More efficient than passing full schema context with every tool call — LLMs can fetch schema once and reuse it, reducing token overhead compared to embedding schema in each validator tool definition.
Executes Regle's validation logic (required, email, minLength, pattern, custom rules) within the MCP server process when invoked by LLM clients, returning structured validation results with error messages and field-level details. The server maintains Regle's validation semantics (async support, custom validators, error formatting) while translating results into MCP-compatible response formats.
Unique: Runs Regle validators server-side via MCP, preserving Regle's validation semantics (async support, custom rules, error formatting) while making them accessible to LLM clients without Vue dependency. Decouples validation logic from UI framework.
vs alternatives: More reliable than asking LLMs to validate forms based on rule descriptions — uses actual Regle validators, ensuring validation behavior matches production Vue forms exactly.
Provides server initialization, configuration, and lifecycle hooks for the MCP server instance, including startup, shutdown, and resource/tool registration. The server handles MCP protocol handshake, capability negotiation, and client connection management, allowing developers to configure which Regle schemas and validators are exposed to connected LLM clients.
Unique: Provides standard MCP server lifecycle management (init, register tools/resources, handle client connections) tailored for Regle schema exposure. Abstracts MCP protocol details from developers configuring form validation services.
vs alternatives: Simpler than building a custom MCP server from scratch — handles protocol boilerplate and resource registration automatically, allowing developers to focus on schema configuration.
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 @regle/mcp-server at 23/100. @regle/mcp-server leads on ecosystem, while Supabase is stronger on 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
+1 more capabilities