| Ecosystem |
| 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Maintains a centralized, searchable registry of available MCP servers by crawling, cataloging, and indexing server metadata including capabilities, installation instructions, and compatibility information. The system aggregates server definitions from multiple sources and exposes them through a unified query interface, enabling developers to discover compatible servers without manual research across fragmented repositories.
Unique: Operates as a meta-MCP (MCP of MCPs) that abstracts the fragmented MCP server ecosystem into a single queryable registry, rather than requiring developers to manually track individual server repositories or maintain local server lists
vs alternatives: Provides centralized discovery for the entire MCP ecosystem in one place, whereas alternatives require developers to search GitHub, documentation sites, or maintain manual server lists
Exposes a remote MCP endpoint (https://mcp.pfvc.io/mcp/) that clients can connect to directly without local installation, handling server lifecycle management, request routing, and connection pooling on behalf of the client. This architecture eliminates the need for developers to run MCP servers locally while maintaining full protocol compatibility with standard MCP clients.
Unique: Implements MCP as a remote-first service with no local installation requirement, using a hosted endpoint that handles all server infrastructure, whereas typical MCP servers require local deployment and dependency management
vs alternatives: Eliminates setup friction compared to self-hosted MCP servers, making it accessible to developers who want discovery without infrastructure overhead
Parses and extracts formal capability schemas from MCP server definitions, including tool signatures, resource types, prompt templates, and supported operations. The system generates standardized documentation that describes what each server can do, what inputs it accepts, and what outputs it produces, enabling developers to understand server capabilities without reading source code.
Unique: Automatically extracts and standardizes capability metadata from heterogeneous MCP servers into a unified schema format, enabling cross-server comparison and automated documentation generation rather than manual curation
vs alternatives: Provides machine-readable capability schemas for the entire MCP ecosystem, whereas alternatives require manual documentation review or source code inspection
Aggregates and surfaces installation instructions, dependency requirements, configuration examples, and setup guides for each MCP server in the registry. The system normalizes these instructions across servers with different package managers, languages, and deployment models, presenting them in a consistent format with platform-specific variants (pip, npm, cargo, Docker, etc.).
Unique: Normalizes installation instructions across servers written in different languages and using different package managers, presenting them in a unified, copy-paste-ready format rather than requiring developers to navigate individual server repositories
vs alternatives: Provides one-stop installation guidance for the entire MCP ecosystem, whereas alternatives require visiting each server's GitHub repository individually
Analyzes MCP server metadata to determine compatibility with specific client versions, Python/Node.js versions, and other system dependencies. The system resolves transitive dependencies, identifies version conflicts, and provides compatibility matrices showing which servers work together without conflicts.
Unique: Provides cross-server dependency resolution and compatibility analysis for the entire MCP ecosystem, enabling developers to understand complex dependency graphs across multiple servers rather than checking each server individually
vs alternatives: Offers ecosystem-wide compatibility analysis that alternatives cannot provide, since they typically focus on individual servers without understanding interactions across the broader MCP landscape
Classifies MCP servers into semantic categories (e.g., data processing, web integration, code tools, knowledge bases) and applies descriptive tags based on server capabilities and use cases. This enables filtering and discovery by functional domain rather than requiring exact server name knowledge, using both automated classification and community-contributed tags.
Unique: Applies multi-dimensional semantic categorization to MCP servers based on functional capabilities and use cases, enabling discovery by domain rather than requiring exact server name knowledge or manual browsing
vs alternatives: Provides semantic search and filtering across the MCP ecosystem, whereas alternatives typically only support keyword search or require developers to know server names in advance
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 AllInOneMCP at 24/100.
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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