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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Implements the Model Context Protocol (MCP) server specification, providing a standardized interface for LLM clients to discover and invoke capabilities through a JSON-RPC 2.0 transport layer. The server handles connection initialization, capability advertisement via the initialize handshake, and maintains bidirectional message routing between client and server implementations throughout the session lifecycle.
Unique: unknown — insufficient data on cq_mini's specific MCP implementation details, server architecture, or differentiation from other MCP servers
vs alternatives: unknown — insufficient data on performance characteristics, feature set, or architectural decisions compared to alternative MCP server implementations
Defines and advertises available tools to MCP clients through a standardized schema format that includes tool names, descriptions, input parameter specifications (with type validation), and return value documentation. The server introspects its capabilities and broadcasts them during the MCP initialize handshake, allowing clients to discover what operations are available without hardcoded knowledge.
Unique: unknown — insufficient data on cq_mini's schema definition approach, whether it uses decorators, configuration files, or runtime introspection
vs alternatives: unknown — insufficient data on schema expressiveness, validation strictness, or developer ergonomics compared to other MCP server implementations
Executes requested tools by routing JSON-RPC call_tool messages to the appropriate handler functions, marshaling input parameters from JSON into native types, executing the tool logic, and serializing results back to JSON for client consumption. Handles error cases by returning structured error responses that preserve stack traces and error context for debugging.
Unique: unknown — insufficient data on cq_mini's tool execution architecture, whether it uses async/await, thread pools, or process isolation
vs alternatives: unknown — insufficient data on execution performance, error handling robustness, or timeout/resource management compared to alternatives
Exposes server-side resources (files, database records, API responses, or computed data) to MCP clients through a resource URI scheme, allowing clients to request and retrieve resource contents without direct file system or database access. Resources are advertised with metadata (MIME type, size, last modified) and served through the read_resource MCP message type with support for partial reads and streaming.
Unique: unknown — insufficient data on cq_mini's resource implementation, whether it supports streaming, caching, or dynamic resource generation
vs alternatives: unknown — insufficient data on resource performance, security model, or feature completeness compared to other MCP servers
Defines reusable prompt templates that MCP clients can invoke to generate structured prompts with variable substitution and formatting. Templates are advertised with input parameter schemas and descriptions, allowing clients to discover and execute them without embedding prompt logic client-side. Supports dynamic prompt generation based on runtime context and tool availability.
Unique: unknown — insufficient data on cq_mini's prompt template implementation, syntax, or feature set
vs alternatives: unknown — insufficient data on template expressiveness, rendering performance, or versioning capabilities compared to alternatives
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 cq_mini at 22/100. cq_mini 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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