| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Paid | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Accepts MCP server metadata (name and URL) via form input and generates formatted, shareable installation guides that reduce setup friction for end users. The system likely parses server metadata from the provided URL, extracts installation requirements, and renders them into a human-readable guide format optimized for distribution across documentation sites, GitHub, and community channels.
Unique: Specifically targets MCP server discovery and installation friction by auto-generating guides from server metadata rather than requiring manual documentation maintenance. Positions installation guides as first-class shareable artifacts in the MCP ecosystem.
vs alternatives: Reduces documentation burden compared to manual README creation or generic installation templates by automating guide generation from live server metadata.
Retrieves and parses MCP server metadata from provided URLs to extract installation requirements, dependencies, and configuration details. The system likely makes HTTP requests to the server endpoint, inspects MCP protocol responses or manifest files, and structures the extracted data for guide generation. This enables dynamic guide creation without hardcoded server-specific logic.
Unique: Implements live metadata extraction from MCP servers rather than static configuration, enabling guides to stay synchronized with server changes without manual intervention.
vs alternatives: More maintainable than static guide templates because it pulls from the source of truth (the server itself) rather than requiring documentation updates in parallel.
Generates stable, shareable URLs for installation guides that persist across requests, enabling users to distribute guide links via documentation, social media, and community channels. The system likely creates a unique identifier for each server-guide combination, stores the generated guide in a database or cache, and returns a canonical URL that resolves to the formatted guide. This decouples guide distribution from the generation process.
Unique: Creates persistent, shareable guide URLs that decouple the guide generation process from distribution, enabling guides to be shared widely without requiring regeneration or server-side state management by the MCP developer.
vs alternatives: More practical than in-memory guide generation because it provides stable URLs suitable for long-term distribution, unlike ephemeral generation endpoints.
Transforms extracted MCP server metadata into visually polished, user-friendly HTML installation guides with consistent styling and layout. The system applies a design template to structured server data, formats installation steps in a readable sequence, and renders the output as a complete HTML document suitable for viewing in browsers or embedding in other pages. This ensures guides have a professional appearance regardless of the source server.
Unique: Applies a unified, professionally-designed template to all MCP server guides, ensuring consistent visual presentation and user experience across the ecosystem rather than relying on individual server documentation quality.
vs alternatives: Produces more polished, consistent guides than asking developers to write their own documentation, and requires less effort than maintaining separate design systems per server.
Provides example MCP servers (e.g., 'Parallel Task MCP') with pre-generated installation guides that demonstrate the guide generation capability and serve as reference implementations. The system likely maintains a curated list of public MCP servers, generates guides for them, and displays these as examples on the website. This enables potential users to see the output format and value proposition without needing to provide their own server.
Unique: Showcases the guide generation capability through live examples of popular MCP servers, enabling potential users to evaluate the service quality and understand the output format before committing their own servers.
vs alternatives: More effective for user onboarding than abstract feature descriptions because it provides concrete, interactive examples of the generated guides.
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 This MCP at 18/100. 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
+1 more capabilities