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| Match Graph | 0 | 0 |
| Pricing | Paid | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
This capability analyzes the current code context within VS Code using an abstract syntax tree (AST) to provide relevant code suggestions. It leverages a local model that understands the project's structure, allowing it to suggest snippets that are not only syntactically correct but also semantically relevant to the existing code. This approach minimizes the need for network calls, enhancing performance and responsiveness.
Unique: Utilizes a local AST parser to provide context-aware suggestions, reducing reliance on external APIs and improving speed.
vs alternatives: Offers faster and more relevant suggestions compared to cloud-based alternatives by processing code locally.
This capability enables developers to refactor code automatically by identifying code smells and suggesting improvements based on best practices. It uses static analysis techniques to evaluate the code structure and dependencies, allowing it to recommend changes that enhance readability and maintainability without altering functionality. The refactoring suggestions are context-aware, ensuring they fit seamlessly into the existing codebase.
Unique: Combines static analysis with context-aware suggestions to provide targeted refactoring advice tailored to the current code state.
vs alternatives: More precise and contextually relevant than generic refactoring tools that do not consider the entire codebase.
This capability provides real-time debugging assistance by analyzing code execution and suggesting potential fixes for errors. It integrates with the VS Code debugger to monitor variable states and control flow, offering insights and recommendations based on common error patterns. The tool can highlight problematic lines of code and suggest corrective actions, streamlining the debugging process for developers.
Unique: Integrates directly with the VS Code debugging environment, providing real-time suggestions based on live code execution.
vs alternatives: More integrated and responsive than standalone debugging tools that require manual input for error resolution.
This capability analyzes the overall structure of a codebase to provide insights into organization, dependencies, and potential areas for improvement. It uses dependency graphs and static analysis to visualize relationships between modules, helping developers understand how changes in one part of the code may affect others. This analysis aids in planning refactoring efforts and improving code organization.
Unique: Employs advanced static analysis techniques to create visual representations of code dependencies, enhancing understanding of project structure.
vs alternatives: Offers deeper insights into project structure compared to traditional code analysis tools that lack visualization capabilities.
This capability facilitates collaborative code reviews by integrating with version control systems to provide inline comments, suggestions, and feedback mechanisms. It uses machine learning to analyze code changes and highlight areas that may require attention based on previous review patterns. This integration streamlines the review process, making it easier for teams to maintain code quality and consistency.
Unique: Integrates machine learning to provide context-aware feedback during code reviews, enhancing team collaboration.
vs alternatives: More effective than traditional code review tools that lack intelligent feedback mechanisms.
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 Kilo Code at 17/100. 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