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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Launches a web-based AI assistant (Claude, Grok) in your default browser while keeping your terminal active, using shell script orchestration to manage process lifecycle and context passing. Implements a non-blocking IPC pattern where the terminal remains responsive while the browser window handles AI interaction, avoiding the context-switching friction of traditional IDE plugins or separate chat windows.
Unique: Uses lightweight Bash scripts to orchestrate browser lifecycle without requiring IDE plugins, language-specific SDKs, or local AI model infrastructure — delegates AI computation to web services while maintaining terminal-first UX
vs alternatives: Lighter weight and less invasive than IDE plugins (no VS Code/JetBrains dependencies) and faster to set up than local LLM runners, but trades automation for simplicity by keeping human-in-the-loop browser interaction
Wraps Claude/Grok web URLs in Bash functions that handle URL construction, parameter encoding, and browser invocation through shell script abstractions. Implements a thin CLI-to-web-UI adapter pattern where shell commands map to pre-configured web endpoints, avoiding the need to manually construct URLs or remember service-specific query parameters.
Unique: Implements URL parameter encoding and browser invocation entirely in Bash without external dependencies (no Python, Node.js, or compiled binaries), making it portable across Unix-like systems and trivial to audit/modify
vs alternatives: Simpler and more portable than Python/Node.js CLI wrappers for the same functionality, but less capable at handling complex state or large context windows due to URL length constraints
Manages browser window lifecycle (launch, focus, close) through shell process control without blocking the terminal or interrupting active shell sessions. Uses background process spawning (&) and optional process detachment (nohup, disown) to ensure the terminal remains responsive while the browser window operates independently, implementing a fire-and-forget pattern for AI interaction.
Unique: Uses standard Bash job control (&, disown, nohup) rather than systemd, launchd, or other OS-specific daemons, ensuring portability and minimal system footprint while maintaining terminal responsiveness
vs alternatives: More lightweight than daemon-based approaches (no systemd service files or launchd plists) but less robust at process lifecycle management — trade-off favors simplicity and portability over reliability
Abstracts different web-based AI services (Claude, Grok) behind a unified Bash interface, allowing users to switch between providers via environment variables or command-line flags without changing core script logic. Implements a simple provider registry pattern where each service has a corresponding URL template and launch function, enabling extensibility for additional AI services.
Unique: Implements provider switching via simple Bash conditionals and environment variables rather than a plugin system or configuration DSL, keeping the codebase minimal and auditable while still supporting multiple services
vs alternatives: More flexible than hardcoded single-service scripts but less sophisticated than plugin architectures (e.g., LangChain providers) — trades advanced features for simplicity and ease of modification
Maintains shell session state (command history, environment variables, working directory) while launching external AI interaction, ensuring developers can resume terminal work without losing context. Implements this through process isolation — the browser window is spawned as a child process that doesn't interfere with the parent shell's state, and the terminal remains in the same directory with the same environment.
Unique: Achieves context preservation through standard Unix process isolation (child processes don't modify parent state) rather than explicit state management or session serialization, making it automatic and zero-configuration
vs alternatives: More transparent than IDE-based approaches (no plugin state to manage) but less integrated — developers must manually manage context passing rather than having automatic code selection or clipboard integration
Provides a lightweight setup process (typically copying Bash scripts to ~/.local/bin or sourcing from ~/.bashrc) with no external package dependencies beyond a standard Unix environment. Implements zero-dependency operation by relying entirely on built-in Bash features and standard Unix utilities (xdg-open, curl, etc.), avoiding the need for package managers, virtual environments, or language-specific runtimes.
Unique: Achieves full functionality with only Bash and standard Unix utilities, avoiding any language-specific runtimes or package managers — makes the tool trivial to install on any Unix-like system and trivial to audit for security
vs alternatives: Faster to install and more portable than Python/Node.js CLI tools, and more auditable than tools with large dependency trees, but less capable at complex features that would normally require external libraries
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 poorcoder at 24/100.
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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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