| Ecosystem |
| 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Discovers available MCP servers from a curated registry and installs them locally with dependency resolution. The tool queries a central registry index, resolves version constraints, downloads server packages from npm or other sources, and configures them for local use. It handles transitive dependency management and validates server compatibility before installation.
Unique: Provides a dedicated MCP-aware registry and discovery layer on top of npm, with MCP-specific validation and configuration rather than treating servers as generic npm packages
vs alternatives: Simpler than manual npm install + configuration because it handles MCP-specific setup and validation in a single command
Provides an interactive command-line interface that guides users through MCP server installation with prompts for configuration options, environment variables, and connection parameters. The tool uses a prompt-based workflow to collect server-specific settings, validates inputs against server schemas, and generates configuration files in the appropriate format (JSON, YAML, or environment files).
Unique: Uses schema-driven prompts that adapt based on server requirements, rather than static questionnaires, enabling context-aware configuration guidance
vs alternatives: More user-friendly than manual JSON editing because it validates inputs and explains each configuration option in context
Manages the lifecycle of installed MCP servers with commands to start, stop, restart, and monitor running instances. The tool spawns server processes, manages stdio/stderr streams, handles graceful shutdown with timeout fallback to force-kill, and tracks process state. It integrates with the host system's process management and provides health-check capabilities to verify server availability.
Unique: Integrates MCP server lifecycle with the installation system, allowing unified management of discovery, installation, and runtime operations in a single tool
vs alternatives: More convenient than managing servers with separate tools (npm start, systemctl, PM2) because it provides a unified interface across all installed servers
Enumerates all installed MCP servers with detailed metadata including version, status, configuration, and capabilities. The tool scans the installation directory, reads server manifests or package.json files, queries running processes, and aggregates information into a human-readable or machine-parseable report. It can filter servers by status, type, or capability and export reports in JSON or table formats.
Unique: Aggregates installation metadata with runtime process state to provide unified visibility into both installed and active servers
vs alternatives: More comprehensive than `npm list` because it includes runtime status and MCP-specific metadata like exposed capabilities
Generates standardized configuration files for MCP servers in formats compatible with Claude Desktop, LLM agents, and other MCP clients. The tool reads server manifests, applies user-provided settings, validates configuration against server schemas, and outputs properly formatted config files (typically JSON or YAML). It supports multiple configuration targets and can generate configuration snippets for different client types.
Unique: Generates MCP-specific configuration with awareness of multiple client types (Claude Desktop, agents, etc.) rather than generic config file generation
vs alternatives: More reliable than manual config editing because it validates against server schemas and ensures compatibility with target clients
Removes installed MCP servers and associated configuration files, environment variables, and process artifacts. The tool identifies all files and directories related to a server, removes them safely with optional backup, updates configuration files to remove server references, and verifies cleanup completion. It can optionally preserve configuration for reinstallation or perform deep cleanup including cached dependencies.
Unique: Provides MCP-aware uninstall that removes both server packages and MCP-specific configuration, not just npm package deletion
vs alternatives: More thorough than `npm uninstall` because it also removes configuration files and updates client configs that reference the server
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 install-mcp at 21/100. install-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