| 0 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 16 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Translates Model Context Protocol requests into Neovim RPC calls via Unix socket communication managed by a NeovimManager singleton. The server implements a three-layer architecture (MCP interface, application logic, socket integration) that maintains a persistent connection to running Neovim instances and serializes/deserializes RPC payloads, enabling AI clients to control Neovim as a remote process without direct binary dependencies.
Unique: Uses official neovim/node-client JavaScript library for RPC communication rather than spawning subprocess or implementing custom RPC protocol, ensuring compatibility with Neovim's native RPC interface and reducing maintenance burden. Implements NeovimManager as a singleton pattern to maintain stateful connection across multiple MCP tool invocations.
vs alternatives: More reliable than shell-based Neovim control (nvim --remote) because it uses native RPC protocol with proper error handling and connection state management, and more lightweight than embedding a full Neovim instance as a subprocess.
Exposes the nvim://buffers resource that lists all open buffers with metadata (filename, line count, modification status) and implements vim_buffer tool to read full buffer content or specific line ranges. The system maintains awareness of which buffers are currently loaded in the editor session, enabling AI clients to query editor state and extract code context without requiring file system access.
Unique: Exposes buffer content through MCP resources (nvim://buffers) rather than only as tool outputs, allowing MCP clients to treat editor buffers as first-class knowledge sources that can be referenced in prompts and context windows. Integrates with Neovim's native buffer management rather than implementing custom file tracking.
vs alternatives: More efficient than file system-based code reading because it accesses already-loaded buffers in memory via RPC, avoiding disk I/O and file permission issues. Provides real-time editor state vs static file snapshots.
Implements vim_visual_select tool that creates visual selections (character, line, or block mode) on specified line ranges, and vim_get_selection that retrieves currently selected text. The tools use Neovim's cursor positioning and mode-setting RPC calls to establish selections, then enable subsequent operations (delete, copy, format) on the selected range. Selections are mode-aware (visual, visual-line, visual-block).
Unique: Exposes Vim's visual selection modes (character, line, block) as programmable operations rather than keystroke sequences, allowing AI clients to perform mode-specific operations that would be difficult to express otherwise. Uses Neovim's cursor and mode RPC API for precise selection control.
vs alternatives: More precise than line-based edits because it supports character-level and block-level selections. More flexible than regex-based operations because it can select arbitrary ranges regardless of content.
Implements vim_set_mark and vim_goto_mark tools for creating and navigating to named marks, and vim_get_register/vim_set_register for accessing Vim's register storage. Marks are stored in Neovim's mark table (nvim_buf_set_mark, nvim_buf_get_mark) and registers are accessed via the register API. This enables AI clients to bookmark positions and store text snippets for later retrieval without external state management.
Unique: Exposes Vim's native mark and register systems as MCP tools rather than implementing custom bookmarking, allowing AI clients to leverage Vim's built-in navigation and storage without external state management. Marks integrate with Neovim's buffer-local mark table.
vs alternatives: More integrated than external bookmarking because it uses Vim's native mark system that persists across editor sessions. More efficient than storing state externally because marks and registers are in-memory and accessed via RPC.
Implements vim_create_tab, vim_close_tab, and vim_switch_tab tools for managing Neovim's tab interface, and vim_split_window/vim_close_window for window management. The tools use Neovim's tab and window RPC API (nvim_command for :tabnew, :split, etc.) to manipulate the editor layout. Tab and window state is queryable through the session resource.
Unique: Exposes Neovim's tab and window system as programmable operations rather than requiring keystroke simulation, allowing AI clients to organize complex multi-file workflows with structured layout management. Uses native Neovim commands (:tabnew, :split) via RPC.
vs alternatives: More reliable than keystroke-based window management because it uses native RPC commands that don't depend on keybindings or editor state. More flexible than fixed layouts because it allows dynamic tab/window creation based on workflow needs.
Implements vim_fold and vim_unfold tools that manage code folding using Neovim's folding API. The tools use Neovim's fold commands (:fold, :unfold) to collapse/expand code regions based on syntax or manual folds. vim_get_folds retrieves fold structure for the current buffer, enabling AI clients to understand code organization and navigate at the structural level rather than line-by-line.
Unique: Exposes Neovim's folding system as a way to understand code structure rather than just for visual organization, allowing AI clients to navigate code at the semantic level (functions, classes) rather than raw line numbers. Integrates with Neovim's foldmethod settings.
vs alternatives: More efficient than reading entire files for structural analysis because folds provide a hierarchical view. More flexible than AST-based analysis because it respects user's Neovim folding configuration.
Exposes neovim_workflow prompt that provides contextual guidance for using the Neovim MCP server effectively. The prompt includes best practices, common patterns, and workflow recommendations tailored to the user's current editor state. Prompts are static templates that MCP clients can include in their system prompts to guide AI behavior when interacting with Neovim.
Unique: Provides MCP prompts that guide AI behavior when using Neovim tools, rather than relying on implicit understanding. Allows MCP clients to include workflow guidance in their system prompts for better AI decision-making.
vs alternatives: More effective than undocumented tools because it provides explicit guidance on when and how to use each capability. More integrated than external documentation because prompts are delivered through MCP protocol.
Implements robust error handling throughout the MCP server with try-catch blocks around all Neovim RPC calls, connection state validation, and graceful error reporting. The NeovimManager singleton maintains connection state and automatically reconnects on socket failures. Errors are caught at the RPC layer and returned as structured error responses with error codes and messages, preventing cascading failures.
Unique: Implements error handling at the RPC layer with connection state validation, ensuring that transient socket failures don't crash the server. Uses NeovimManager singleton to maintain connection state across multiple tool invocations.
vs alternatives: More reliable than naive RPC calls because it validates connection state and handles socket errors gracefully. More informative than silent failures because it returns structured error responses with context.
+8 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 mcp-neovim-server 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