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| Match Graph | 0 | 0 |
| Pricing | Paid | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Maintains a searchable registry of MCP (Model Context Protocol) servers contributed by the community. The system crawls, indexes, and catalogs available MCP server implementations with metadata including server name, description, capabilities, and repository links. This enables developers to discover compatible MCP servers without manually searching GitHub or documentation.
Unique: Provides a centralized, searchable catalog specifically for MCP servers rather than requiring developers to manually search GitHub or documentation sites. Implements community-driven curation with metadata standardization for MCP-specific attributes.
vs alternatives: More discoverable than GitHub search alone because it aggregates MCP servers in one place with standardized metadata and filtering, reducing friction for developers evaluating MCP ecosystem options.
Implements a search engine that indexes MCP server names, descriptions, capabilities, and metadata to enable fast keyword-based discovery. The search likely uses inverted indexing or similar full-text search patterns to match user queries against the catalog and return ranked results with relevance scoring.
Unique: Provides MCP-specific full-text search optimized for server discovery rather than generic web search. Likely indexes MCP-specific fields (capabilities, protocol version, authentication methods) to improve relevance for MCP use cases.
vs alternatives: More targeted than generic GitHub search because it understands MCP server structure and metadata, returning more relevant results for developers looking for specific MCP integrations.
Collects and standardizes metadata from diverse MCP server sources (GitHub repositories, documentation, server manifests) into a consistent schema. This involves parsing repository information, extracting capability descriptions, normalizing version information, and organizing data for searchable indexing. The system likely uses web scraping, API calls, or community submission forms to gather and validate server information.
Unique: Implements MCP-specific metadata schema that captures protocol-relevant attributes (supported MCP versions, authentication methods, resource types, tool definitions) rather than generic software metadata. Likely includes automated validation to ensure servers conform to MCP specification requirements.
vs alternatives: More comprehensive than manual GitHub browsing because it extracts and standardizes MCP-specific technical details that developers need to evaluate server compatibility, reducing evaluation friction.
Provides a mechanism for developers to submit new MCP servers to the registry, likely through pull requests, web forms, or API endpoints. The system validates submissions against MCP specifications, checks for duplicates, and integrates approved servers into the catalog. This enables community-driven growth of the MCP ecosystem without requiring centralized development effort.
Unique: Implements a community-driven registry model where server developers can self-submit, reducing centralized maintenance burden. Likely uses GitHub pull requests or similar version-controlled workflows to maintain transparency and enable community review of submissions.
vs alternatives: More scalable than a manually-maintained registry because it enables community contributions, allowing the MCP ecosystem to grow organically without requiring a dedicated team to catalog every new server.
Categorizes and tags MCP servers by their capabilities, supported integrations, and features (e.g., 'database-access', 'file-operations', 'web-search', 'code-execution'). This enables developers to filter and discover servers by functional category rather than searching by name. The system likely maintains a taxonomy of MCP capabilities and maps servers to relevant tags.
Unique: Implements MCP-specific capability taxonomy that reflects the protocol's resource and tool model rather than generic software categorization. Likely includes tags for MCP-specific features like 'resource-access', 'tool-definitions', 'sampling-support', and 'streaming-support'.
vs alternatives: More useful than generic software categorization because it captures MCP-specific capabilities that developers need to evaluate server compatibility with their MCP-based systems.
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 MCP.ing at 22/100. MCP.ing leads on quality, while Supabase is stronger on ecosystem. Supabase also has a free tier, making it more accessible.
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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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