| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Enables Cursor IDE to pause code generation and request user input via a bidirectional MCP protocol bridge. The server implements a request-response pattern where generation can be suspended, user input collected through Cursor's UI, and the response injected back into the generation context. This allows multi-turn interactive workflows where AI-generated code can ask clarifying questions mid-generation rather than requiring all context upfront.
Unique: Implements a synchronous request-response MCP bridge that suspends Cursor's generation pipeline and surfaces user input prompts directly in the IDE UI, rather than requiring separate UI windows or external tools. Uses MCP's bidirectional communication pattern to maintain generation context across user interactions.
vs alternatives: Unlike generic MCP tools that only provide read-only data, this server enables true interactive generation workflows within Cursor by blocking and resuming the generation pipeline based on user responses.
Implements a Model Context Protocol (MCP) server that registers as a tool provider within Cursor's MCP ecosystem. The server exposes input prompting as a callable tool through MCP's standardized schema, allowing Cursor's code generation engine to discover and invoke user input requests using the same mechanism as other MCP tools. Handles MCP message serialization, tool schema registration, and lifecycle management.
Unique: Implements MCP server boilerplate and tool registration patterns specifically optimized for Cursor's MCP integration, handling the full lifecycle from server startup through tool discovery and invocation without requiring developers to understand low-level MCP protocol details.
vs alternatives: Provides a minimal, focused MCP server implementation compared to general-purpose MCP frameworks, reducing complexity and startup overhead for the specific use case of interactive user input during code generation.
Maintains the code generation context and conversation history across multiple user input requests, allowing subsequent generation steps to reference previous responses and generated code. The server preserves the MCP session state and passes context back to Cursor's generation engine, enabling multi-turn interactive workflows where each user input informs the next generation step. Implements context threading through MCP's message protocol.
Unique: Preserves generation context through MCP's stateful message protocol rather than relying on Cursor's internal context management, enabling user input prompts to be fully aware of prior generation decisions and user responses without requiring explicit context passing.
vs alternatives: Unlike stateless tool calling patterns, this capability maintains conversation history across user input cycles, enabling truly interactive generation workflows rather than isolated single-turn prompts.
Bridges MCP user input requests to Cursor's native UI components, displaying input prompts in Cursor's interface and collecting responses through standard UI patterns (text input dialogs, selection menus, etc.). The server communicates input requirements to Cursor via MCP, and Cursor handles rendering and user interaction, then returns responses through the MCP protocol. This avoids spawning external windows or requiring custom UI implementation.
Unique: Leverages Cursor's native MCP UI capabilities to render input prompts directly in the IDE rather than spawning separate windows or requiring custom UI implementation, creating a seamless integrated experience.
vs alternatives: Provides better UX than tools requiring external input windows or CLI prompts, and simpler implementation than tools building custom UI frameworks.
Implements a synchronous blocking pattern where code generation pauses at user input requests, waits for user response through Cursor's UI, and resumes with the collected input. The MCP server coordinates the pause-wait-resume cycle by blocking the MCP request handler until user input is available, then returning the response to unblock generation. This ensures generation cannot proceed without user input, maintaining strict ordering and preventing race conditions.
Unique: Implements explicit blocking synchronization for code generation pipelines rather than using async callbacks or event-driven patterns, ensuring strict ordering and preventing generation from proceeding without user input.
vs alternatives: Provides stronger guarantees about generation ordering compared to async patterns, at the cost of increased latency and reduced parallelism.
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 User Prompt MCP at 20/100.
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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
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