| 0 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Provides a TypeScript template structure for building ModelContextProtocol servers that expose three core MCP resource types: tools (callable functions), prompts (reusable instruction templates), and resources (static/dynamic data). The template includes boilerplate for request routing, error handling, and MCP protocol compliance, enabling developers to extend each resource type by implementing handler functions that conform to the MCP specification.
Unique: Unified template covering all three MCP resource types (tools, prompts, resources) in a single TypeScript codebase, with explicit handler patterns for each type rather than generic function-calling abstractions
vs alternatives: Simpler onboarding than raw MCP SDK usage because it provides working examples of tools, prompts, and resources in one place, reducing trial-and-error when learning the protocol
Implements a request router that maps incoming MCP tool-call requests to handler functions based on tool name and parameter schema. The template provides a pattern for defining tools with typed parameters (using JSON Schema), validating incoming requests against those schemas, and routing to the appropriate handler function. Responses are wrapped in the MCP JSON-RPC response envelope with proper error handling for missing tools or invalid parameters.
Unique: Explicit handler pattern with JSON Schema parameter validation built into the template, rather than relying on generic function-calling abstractions or code introspection
vs alternatives: More transparent than OpenAI function calling because the schema and handler are co-located and human-readable, making it easier to audit what tools are exposed and how they behave
Provides a pattern for defining reusable prompt templates as MCP resources with variable placeholders, which can be retrieved and rendered by clients. The template includes examples of how to structure prompt definitions (name, description, arguments schema) and how to implement a handler that substitutes variables into template text. Clients can query available prompts and request rendered versions with specific variable values, enabling prompt reuse across multiple LLM interactions.
Unique: Treats prompts as first-class MCP resources with discoverable metadata and parameterized rendering, rather than embedding them in client code or storing them in separate configuration files
vs alternatives: More discoverable and version-controlled than hardcoded prompts because they're exposed via MCP and can be queried by clients, enabling dynamic prompt selection and A/B testing
Implements a resource registry pattern where static or dynamically-generated data (files, API responses, database records) are exposed as named MCP resources with URI-based querying. The template provides handlers for listing available resources and retrieving specific resource content by URI, with support for both text and binary content types. Resources can be static (file-based) or dynamic (computed on-demand), enabling clients to access backend data without direct API access.
Unique: Exposes resources as first-class MCP entities with discoverable metadata and URI-based retrieval, rather than embedding data in tool responses or requiring clients to make separate API calls
vs alternatives: More flexible than static file serving because resources can be computed dynamically, filtered by client request, or aggregated from multiple sources while maintaining a simple URI-based interface
Provides boilerplate for handling the ModelContextProtocol specification, including JSON-RPC 2.0 request/response envelope formatting, error code mapping, and protocol version negotiation. The template includes handlers for MCP lifecycle messages (initialize, ping) and ensures all tool, prompt, and resource responses are wrapped in the correct JSON-RPC format with proper error handling for malformed requests, missing methods, and internal errors.
Unique: Provides explicit JSON-RPC envelope handling and MCP protocol compliance patterns in the template, reducing the chance of subtle protocol violations that break client compatibility
vs alternatives: More reliable than building from scratch because it includes tested patterns for error handling and response formatting, reducing debugging time when integrating with MCP clients
Includes TypeScript type definitions for all MCP request and response structures (tools, prompts, resources, errors), enabling compile-time type checking and IDE autocomplete for handler implementations. The template uses discriminated unions for different request types and ensures handlers return properly-typed responses that match the MCP specification, reducing runtime errors from malformed responses.
Unique: Provides comprehensive TypeScript types for the entire MCP protocol surface, including discriminated unions for different request types, rather than generic 'any' types or minimal type coverage
vs alternatives: Catches more errors at compile time than JavaScript-based MCP servers because TypeScript enforces correct response structures before runtime, reducing integration bugs with clients
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 @ignitionai/mcp-template at 22/100. @ignitionai/mcp-template 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
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