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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 10 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Implements a complete reference server showcasing all four core MCP capability primitives (Tools, Resources, Prompts, Roots) through a unified TypeScript SDK interface. The server exposes these capabilities via JSON-RPC 2.0 protocol over stdio/SSE transports, allowing LLM clients to discover and invoke server-side functionality through standardized message schemas. This is an educational implementation designed to teach developers the exact patterns and SDK usage required to build their own MCP servers.
Unique: Serves as the official MCP reference implementation maintained by the MCP steering group, demonstrating all four protocol primitives (Tools, Resources, Prompts, Roots) in a single cohesive TypeScript codebase using the canonical MCP SDK patterns, rather than scattered examples across multiple repositories
vs alternatives: More authoritative and complete than third-party MCP examples because it's the official reference maintained alongside the protocol specification itself, ensuring alignment with the latest MCP standards
Exposes callable tools to LLM clients through a schema-based function registry that defines tool names, descriptions, input schemas (JSON Schema format), and handler implementations. The server registers tools with the MCP SDK, which serializes them into the protocol's tool definition format and responds to tool_call requests with execution results. Tools are invoked through a standardized call pattern where the client sends tool name + parameters, the server executes the handler, and returns structured results or errors.
Unique: Uses the MCP SDK's native tool registration pattern with JSON Schema validation, which provides automatic schema serialization and client-side discovery without requiring manual OpenAI/Anthropic function-calling API adapters, making it transport-agnostic and protocol-native
vs alternatives: Simpler than building tool-calling adapters for each LLM provider because MCP handles schema standardization and client discovery, allowing one tool definition to work across any MCP-compatible client
Exposes static or dynamic content as resources through a URI-based addressing scheme, where clients request resources by URI and the server returns content (text, code, structured data) along with MIME type metadata. Resources are registered with the MCP SDK with URI templates, descriptions, and content handlers that fetch or generate content on demand. The server maintains a resource list that clients can query to discover available resources, enabling LLMs to reference external knowledge or data sources.
Unique: Implements resources as first-class MCP primitives with URI-based addressing and automatic client discovery, rather than embedding content in prompts or requiring clients to make separate HTTP requests, enabling cleaner separation of concerns between LLM logic and data access
vs alternatives: More efficient than prompt-based context injection because resources are fetched on-demand and can be updated server-side without redeploying the LLM, and more standardized than custom HTTP endpoints because MCP handles discovery and transport
Exposes reusable prompt templates through the MCP SDK that clients can discover and instantiate with variable substitution. Prompts are registered with names, descriptions, argument schemas, and template content that supports variable placeholders (e.g., {{variable}}). When a client requests a prompt, the server substitutes provided arguments into the template and returns the rendered prompt text. This enables LLM clients to use server-defined prompts for consistent, parameterized interactions.
Unique: Treats prompts as discoverable, versioned server-side resources rather than client-side strings, enabling centralized prompt management and allowing LLM clients to request domain-specific prompts by name without hardcoding template text
vs alternatives: More maintainable than embedding prompts in client code because prompt updates happen server-side, and more discoverable than prompt libraries because clients can query available prompts and their argument schemas
Declares workspace or project roots that define the scope of resources and tools available to LLM clients, allowing servers to communicate which directories, repositories, or logical boundaries the client should operate within. Roots are registered with the MCP SDK and communicated to clients during capability discovery, enabling clients to understand the context boundaries for file operations, resource access, and tool execution. This is particularly useful for multi-project environments where different clients need different access scopes.
Unique: Implements roots as a first-class MCP primitive for declaring workspace context boundaries, rather than relying on implicit filesystem permissions or client-side configuration, enabling servers to explicitly communicate scope to clients during capability discovery
vs alternatives: Clearer than implicit filesystem permissions because roots are explicitly declared and discoverable, and more flexible than hardcoded paths because roots can be configured per server instance
Abstracts the underlying transport mechanism (stdio, SSE, WebSocket) behind a unified JSON-RPC 2.0 message protocol, allowing MCP servers to communicate with clients regardless of transport layer. The MCP SDK handles serialization/deserialization of JSON-RPC messages, request/response correlation, and error handling, while the server implementation remains transport-agnostic. This enables the same server code to work over stdio (for local CLI tools), SSE (for HTTP), or WebSocket (for real-time connections) without modification.
Unique: Provides transport abstraction through the MCP SDK's unified interface, allowing servers to be written once and deployed over stdio, SSE, or WebSocket without code changes, rather than requiring separate implementations per transport like traditional RPC frameworks
vs alternatives: More flexible than REST APIs because transport is abstracted and clients can choose the best transport for their environment, and more standardized than custom RPC protocols because it uses JSON-RPC 2.0 with MCP-specific extensions
Implements the MCP protocol's capability discovery mechanism where servers advertise available tools, resources, prompts, and roots to clients through standardized schema messages. When a client connects, the server responds to discovery requests with complete capability definitions including names, descriptions, input/output schemas, and metadata. This enables clients to dynamically discover what the server can do without hardcoding capability lists, and to validate parameters before invoking tools or requesting resources.
Unique: Implements discovery as a core protocol feature with standardized schema advertisement, rather than requiring clients to hardcode capability lists or parse documentation, enabling true dynamic capability discovery and client-side validation
vs alternatives: More discoverable than REST APIs with OpenAPI specs because discovery is built into the protocol and happens at connection time, and more flexible than static tool lists because capabilities can be updated server-side
Provides working code examples demonstrating best practices for using the MCP TypeScript SDK, including proper server initialization, capability registration, error handling, and transport configuration. The Everything server serves as a teaching tool showing how to structure MCP server code, organize handlers, define schemas, and respond to client requests. Developers can study the source code to understand SDK patterns before building their own servers, reducing the learning curve for MCP adoption.
Unique: Serves as the official MCP reference implementation maintained by the MCP steering group, providing authoritative examples of SDK usage patterns that are guaranteed to align with the current protocol specification and SDK API
vs alternatives: More authoritative than third-party tutorials because it's maintained alongside the SDK itself, ensuring examples stay current with API changes and best practices
+2 more capabilities
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 Everything at 22/100.
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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