| Ecosystem |
| 1 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
This capability utilizes a local MCP server architecture to perform semantic analysis of Go code, leveraging the Go language's type system and syntax tree. It processes code in real-time, providing context-aware suggestions and insights that are deterministic and tailored to the specific project structure, unlike cloud-based solutions that may lack local context. The local installation ensures that sensitive codebases remain private and secure.
Unique: The use of a local MCP server allows for real-time, context-aware analysis without the latency or privacy concerns of cloud-based solutions.
vs alternatives: More secure and faster than cloud-based Go analysis tools since it operates entirely on local resources.
This capability provides deterministic code completion by analyzing the current context of the Go code being written. It uses the local semantic understanding of the codebase to suggest completions that are relevant to the specific project, ensuring that the suggestions are accurate and contextually appropriate. This is achieved through an integration with the Go language server protocol, which enables deep understanding of the code structure.
Unique: Utilizes a local server to provide real-time, context-sensitive completions based on the entire project structure, unlike many cloud-based tools.
vs alternatives: Offers more relevant completions than IDE-integrated solutions because it fully understands the local project context.
This capability continuously analyzes Go code for errors as it is being written, providing immediate feedback to the developer. It leverages the Go compiler's error reporting mechanisms and integrates them into the local MCP server, allowing for fast detection of syntax and semantic errors. This ensures that developers can correct issues on-the-fly, improving overall code quality and reducing debugging time.
Unique: Integrates real-time error detection directly into the coding process via a local server, ensuring immediate feedback without the need for manual compilation.
vs alternatives: More immediate and context-aware than traditional IDE error checks, which often require manual compilation.
This capability automatically generates documentation for Go projects based on the code structure and comments within the code. It uses the local server to parse the Go code and extract relevant information, creating documentation that is tailored to the specific project context. This ensures that the generated documentation is accurate and reflects the current state of the codebase, which is often a challenge with static documentation tools.
Unique: Automatically generates documentation based on real-time code analysis, ensuring it reflects the latest changes in the codebase.
vs alternatives: More accurate and contextually relevant than traditional documentation generators that rely on static analysis.
This capability provides tools for refactoring Go code within the local environment, allowing developers to make structural changes while maintaining code integrity. It uses the local semantic understanding of the codebase to suggest safe refactoring options, ensuring that changes do not introduce errors. This is achieved through integration with the Go language server, which understands the relationships between code components.
Unique: Offers localized refactoring suggestions based on a deep understanding of the project's code structure, ensuring safe and effective changes.
vs alternatives: More reliable than generic refactoring tools that lack project-specific context.
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 gopls-mcp at 30/100.
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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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