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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 9 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
This capability utilizes an SQLite-based system for intelligent memory management, allowing for context classification, priority management, and session restoration. It employs a context compression algorithm that prioritizes important information, enabling efficient storage and retrieval of session data. The concurrent control and indexing features of SQLite enhance the performance and reliability of memory operations.
Unique: Integrates context compression with SQLite for efficient long-term storage and retrieval, unlike alternatives that may use simpler key-value stores.
vs alternatives: More efficient in managing large contexts compared to traditional in-memory solutions.
This capability leverages Abstract Syntax Tree (AST) analysis to perform in-depth code navigation and symbol tracking across multiple programming languages. By utilizing a project caching system, it optimizes performance through LRU caching, allowing for quick access to symbol definitions and references without re-parsing the entire codebase.
Unique: Utilizes AST-based analysis rather than regex, allowing for more accurate symbol tracking and navigation.
vs alternatives: Faster and more reliable than regex-based tools for multi-language codebases.
This capability assesses code quality through various metrics such as cyclomatic complexity and coupling/cohesion analysis. It generates actionable improvement suggestions based on a comprehensive evaluation of code structure and quality scores, which are presented in an easy-to-understand grading system.
Unique: Combines multiple quality metrics into a single grading system, providing a holistic view of code quality.
vs alternatives: More comprehensive than single-metric tools, offering actionable insights for improvement.
This capability automates the generation of product requirements documents (PRDs) and user stories, utilizing a structured approach to requirements analysis. It incorporates MoSCoW prioritization to help teams focus on essential features while generating a detailed roadmap for development.
Unique: Automates PRD and user story generation based on structured input, reducing manual effort significantly.
vs alternatives: Faster and more systematic than traditional manual planning processes.
This capability supports structured problem-solving by breaking down complex issues into manageable steps. It generates thinking chains that guide users through a logical reasoning process, facilitating multiple perspectives in analysis and execution planning.
Unique: Facilitates multi-perspective analysis and structured reasoning, unlike simpler brainstorming tools.
vs alternatives: More systematic than traditional brainstorming methods, providing clear execution paths.
This capability automatically enhances vague prompts by converting them into more specific requests, improving the quality of input for AI models. It evaluates prompts based on clarity, specificity, and contextuality, ensuring that users can craft effective queries.
Unique: Automatically enhances prompts using a structured evaluation framework, improving interaction quality with AI models.
vs alternatives: More systematic than manual prompt crafting, providing clear guidelines for improvement.
This capability automates web debugging by capturing console logs and analyzing network requests in real-time. It supports multiple browsers, allowing developers to monitor and inspect web applications effectively across different environments.
Unique: Captures console logs and network requests in a unified interface, unlike traditional debugging tools that may require manual inspection.
vs alternatives: More integrated and user-friendly than standalone debugging tools.
This capability provides ASCII art previews of UI layouts, allowing developers to visualize their designs before coding. It supports multiple layout types and responsive previews, helping teams confirm structure and design choices early in the development process.
Unique: Provides ASCII previews that allow for quick design validation without needing a full graphical interface.
vs alternatives: Faster and more accessible than traditional UI design tools that require complex setups.
+1 more capabilities
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 ssd-ai at 34/100.
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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
+1 more capabilities