| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Provides real-time word definition retrieval through the Model Context Protocol, enabling Claude and other MCP-compatible clients to query dictionary data without direct API calls. Implements MCP's tool-calling interface to expose dictionary operations as callable functions within LLM conversations, abstracting HTTP requests and response parsing into standardized protocol messages.
Unique: Exposes dictionary functionality through MCP's standardized tool-calling protocol rather than as a standalone API, enabling seamless integration into Claude and other MCP-compatible LLM workflows without requiring separate authentication or client-side HTTP handling
vs alternatives: Simpler integration than direct dictionary APIs because MCP handles protocol negotiation and tool discovery automatically, while maintaining compatibility across multiple LLM providers that support MCP
Implements MCP's tool definition protocol to advertise dictionary capabilities with JSON Schema specifications, allowing MCP clients to discover available operations, parameter requirements, and response formats through introspection. Uses MCP's standardized tool metadata format to enable automatic UI generation and parameter validation in compatible clients.
Unique: Leverages MCP's built-in tool schema advertisement mechanism to enable automatic client-side discovery and validation, eliminating the need for out-of-band documentation or manual parameter specification
vs alternatives: More discoverable than REST APIs because MCP clients can introspect available tools at runtime, and more maintainable than custom tool registries because schema definitions are standardized by the MCP protocol
Implements MCP server initialization, request handling, and graceful shutdown following the MCP protocol lifecycle. Manages bidirectional message exchange with MCP clients, handles tool invocation requests, and maintains server state across multiple client connections. Provides error handling and response formatting compliant with MCP message specifications.
Unique: Implements the full MCP server lifecycle including initialization handshake, request routing, and graceful shutdown, abstracting away MCP protocol complexity from the dictionary logic layer
vs alternatives: More robust than ad-hoc HTTP servers because MCP protocol handles connection management and message framing standardly, reducing boilerplate and potential protocol violations
Abstracts dictionary data retrieval behind a pluggable interface, allowing different dictionary backends (online APIs, local databases, or embedded data) to be swapped without changing the MCP server code. Handles data normalization and formatting to present consistent definition structures to clients regardless of underlying source.
Unique: Provides a pluggable backend interface that decouples dictionary data sources from the MCP protocol implementation, enabling runtime switching between different dictionary providers without server restart
vs alternatives: More flexible than hardcoded dictionary APIs because new sources can be added by implementing a simple interface, and more maintainable than monolithic implementations because business logic is separated from data access
Implements optional caching layer for frequently requested word definitions to reduce latency and backend load. Uses in-memory or persistent cache storage to serve repeated lookups without querying the underlying dictionary source, with configurable TTL and cache invalidation strategies.
Unique: Implements transparent caching at the MCP server level, allowing clients to benefit from cache hits without awareness of caching logic, while maintaining consistency with the underlying dictionary source
vs alternatives: More efficient than client-side caching because a single server cache serves all connected clients, reducing redundant lookups and backend load compared to each client maintaining its own cache
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 dictionary-mcp at 24/100. dictionary-mcp 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