| 0 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Enables Claude and other MCP-compatible clients to deploy applications to 4EVERLAND hosting infrastructure by translating natural language deployment requests into 4EVERLAND API calls. Implements the Model Context Protocol as a server that exposes 4EVERLAND-specific tools, allowing AI agents to orchestrate deployments without direct API knowledge or credential management in client code.
Unique: Implements 4EVERLAND-specific MCP server that bridges conversational AI (Claude) directly to 4EVERLAND's hosting API, using MCP's standardized tool-calling protocol to abstract away API complexity and credential handling from the client layer.
vs alternatives: Provides native 4EVERLAND integration through MCP (vs. manual API calls or generic deployment tools), enabling AI agents to deploy without custom integrations while maintaining credential isolation at the server level.
Exposes 4EVERLAND hosting operations (deploy, list projects, check status, etc.) as standardized MCP tools with JSON schemas that MCP clients can discover and invoke. The server implements MCP's tool registry pattern, allowing clients to introspect available operations, their parameters, and return types before execution, enabling safe tool composition and error handling in agent workflows.
Unique: Implements MCP's standardized tool registry pattern specifically for 4EVERLAND, allowing clients to discover and validate operations through JSON Schema before execution, rather than relying on documentation or trial-and-error.
vs alternatives: Provides schema-driven tool discovery (vs. unstructured API documentation), enabling AI clients to safely compose multi-step workflows with validation and error handling built in.
Manages 4EVERLAND API credentials at the MCP server level, accepting credentials once during initialization and using them to authenticate all subsequent API calls on behalf of MCP clients. This pattern isolates sensitive credentials from client code and prevents credential leakage through chat logs or client-side storage, implementing a credential proxy pattern where the server acts as a trusted intermediary.
Unique: Implements a credential proxy pattern where the MCP server holds 4EVERLAND credentials and authenticates API calls server-side, preventing credentials from being passed through MCP client requests or exposed in chat logs.
vs alternatives: Isolates credentials at the server layer (vs. client-side credential management), reducing exposure surface and enabling safe multi-user deployments without sharing secrets through chat interfaces.
Orchestrates the deployment workflow for applications to 4EVERLAND, accepting deployment requests with repository/application metadata and translating them into 4EVERLAND API calls that handle build, configuration, and hosting setup. The server manages the deployment lifecycle, polling deployment status, and returning deployment URLs and configuration details to the client, abstracting away 4EVERLAND's internal deployment state machine.
Unique: Implements deployment orchestration as an MCP tool that abstracts 4EVERLAND's deployment state machine, handling polling, status tracking, and result aggregation server-side so clients receive a simple request-response interface rather than managing async deployment lifecycle.
vs alternatives: Provides synchronous deployment interface (vs. manual 4EVERLAND dashboard polling), enabling AI agents to deploy and immediately retrieve deployment URLs without client-side async state management.
Provides tools to list all projects deployed to 4EVERLAND and query their current status, build history, and deployment metadata. The server queries 4EVERLAND's project API and aggregates results into structured data that MCP clients can parse and present to users, enabling visibility into deployment history and current application state without requiring direct 4EVERLAND dashboard access.
Unique: Exposes 4EVERLAND's project and deployment status APIs through MCP tools, aggregating project metadata and status into structured data that MCP clients can query and present without requiring users to access the 4EVERLAND dashboard.
vs alternatives: Provides conversational access to deployment status (vs. manual dashboard navigation), enabling AI agents to monitor and report on deployments as part of larger workflows.
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 4everhosting-mcpserver at 22/100. 4everhosting-mcpserver 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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