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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 10 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Exposes tmux session hierarchy through MCP resource protocol, allowing AI assistants to discover and inspect all active sessions with metadata including session names, window counts, creation timestamps, and attachment status. Implements resource subscription pattern via @modelcontextprotocol/sdk to enable real-time session state synchronization and dynamic resource updates when sessions are created or destroyed.
Unique: Implements MCP resource protocol for tmux introspection rather than simple command wrapping, enabling Claude Desktop to maintain a persistent view of session state through resource subscriptions and change notifications. Uses tmux list-sessions with format strings to extract structured metadata without parsing text output.
vs alternatives: Provides standardized MCP integration for Claude Desktop whereas shell scripts or REST APIs require custom integration work; resource-based architecture enables real-time state awareness vs polling-based alternatives.
Executes shell commands in tmux panes without blocking the MCP server, returning a command ID immediately and allowing result retrieval via separate resource lookup. Implements fire-and-forget execution pattern with optional polling via tmux://command/{commandId}/result resources, supporting both synchronous workflows (wait for completion) and asynchronous patterns (fire and check later). Handles shell-specific exit code detection through configurable --shell-type parameter to correctly identify command success/failure across bash, zsh, and fish.
Unique: Decouples command execution from result retrieval through MCP resource protocol, enabling non-blocking execution patterns where the AI assistant can fire commands and poll results independently. Uses shell-specific exit code markers (e.g., echo $? for bash) to reliably detect command completion and success status across different shell environments.
vs alternatives: Provides true asynchronous execution with deferred result retrieval vs synchronous SSH/exec alternatives that block until completion; shell-type configuration ensures accurate exit code detection across heterogeneous environments vs generic command wrappers that assume single shell type.
Captures the current visible content of a tmux pane with optional ANSI color code preservation, enabling AI assistants to read terminal output including colored text, syntax highlighting, and styled formatting. Implements configurable capture modes via capture-pane tool that can preserve raw ANSI escape sequences or strip them for plain text, supporting both human-readable colored output and machine-parseable plain text depending on use case. Handles pane history buffer retrieval to capture scrollback content beyond the visible viewport.
Unique: Provides dual-mode capture (colored vs plain text) via single tool interface, allowing AI assistants to choose between human-readable colored output and machine-parseable plain text. Uses tmux capture-pane with -p (print) and -S (start line) flags to efficiently retrieve both visible viewport and scrollback history without spawning separate processes.
vs alternatives: Preserves ANSI color codes for semantic understanding vs plain text alternatives that lose formatting context; supports scrollback history retrieval vs simple screen capture that only shows visible content.
Creates, splits, and destroys tmux panes within windows through MCP tools, enabling AI assistants to dynamically manage terminal layout and organize command execution across multiple panes. Implements split-pane operation with configurable split direction (horizontal/vertical) and target pane selection, allowing creation of new panes for parallel execution. Supports pane destruction via kill-pane tool with optional confirmation to prevent accidental data loss.
Unique: Exposes tmux pane splitting and killing as MCP tools with structured input/output, enabling AI assistants to programmatically manage terminal layout without shell command knowledge. Uses tmux split-window and kill-pane commands with format string parsing to return new pane identifiers for subsequent operations.
vs alternatives: Provides structured pane management vs manual tmux commands that require shell knowledge; enables dynamic layout creation during AI workflows vs static pre-configured layouts.
Executes commands in tmux panes with raw mode enabled, allowing interactive applications like REPLs, text editors, and TUI tools to receive input and maintain state across multiple interactions. Implements key injection without automatic Enter appending, enabling navigation of interactive menus and TUI applications through arrow keys and special characters. Maintains pane state between command invocations, allowing AI assistants to interact with long-running interactive sessions (Python REPL, Node REPL, vim, etc.).
Unique: Supports raw mode execution with key injection without Enter, enabling stateful interaction with interactive applications vs simple command execution that assumes line-based input. Maintains pane state across multiple invocations, allowing AI assistants to build multi-turn conversations with REPLs and interactive tools.
vs alternatives: Enables interactive REPL workflows vs batch command execution that cannot maintain state; key injection without Enter supports TUI navigation vs line-based alternatives limited to simple commands.
Creates and destroys tmux windows within sessions through MCP tools, enabling AI assistants to organize command execution across multiple windows within a single session. Implements window creation with optional command execution in the new window, allowing immediate setup of new windows for specific tasks. Supports window destruction via kill-window tool with proper cleanup of all contained panes.
Unique: Exposes tmux window creation and destruction as MCP tools with structured input/output, enabling AI assistants to organize workflows across multiple windows without shell command knowledge. Uses tmux new-window and kill-window commands with format string parsing to return window identifiers.
vs alternatives: Provides structured window management vs manual tmux commands; enables dynamic window creation during workflows vs static pre-configured layouts.
Creates, discovers, and destroys tmux sessions through MCP tools and resources, enabling AI assistants to manage the top-level session hierarchy. Implements session creation with optional initial command and window setup, session discovery via list-sessions and find-session tools with metadata extraction, and session termination via kill-session. Uses tmux list-sessions with format strings to extract structured metadata (session name, window count, creation time, attachment status) without text parsing.
Unique: Implements MCP resource protocol for session discovery with structured metadata extraction via format strings, enabling AI assistants to maintain awareness of session state without text parsing. Supports session creation with initial command setup, allowing immediate task execution in new sessions.
vs alternatives: Provides structured session management vs manual tmux commands; format string-based metadata extraction is more reliable than text parsing for session discovery.
Implements a complete MCP server using @modelcontextprotocol/sdk that exposes tmux functionality through standardized MCP primitives (tools, resources, prompts). Operates as a Node.js process communicating with Claude Desktop via stdio transport, translating MCP protocol requests into tmux commands and returning structured responses. Declares server capabilities including resource subscription support, tool change notifications, and logging, enabling dynamic resource updates and real-time state synchronization.
Unique: Implements full MCP server specification with resource subscription support and capability declaration, enabling Claude Desktop to maintain persistent awareness of tmux state. Uses stdio transport for communication, allowing seamless integration with Claude Desktop's MCP client without network configuration.
vs alternatives: Provides standardized MCP integration vs custom Claude plugins that require separate maintenance; resource subscription enables real-time state awareness vs polling-based alternatives.
+2 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 Tmux at 26/100.
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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