Delegates mechanical text-to-text tasks to a local LLM to save Claude subscription quota, reading files server-side so large content never enters Claude's context.
Enables running MCP tools against local MLX models on your Mac, with hardware-aware configuration, CLI streaming, and a dashboard for routing and monitoring.
Routes code generation tasks between local models and Claude Cloud, optimizing cost by handling simple tasks locally and reserving cloud thinking for complex tasks.
A thin MCP server that delegates lightweight tasks from Claude Code or any MCP-compatible client to local or cloud LLMs via LiteLLM, supporting models like Ollama and cloud APIs as subagents.
Unified MCP server for managing local model runtimes (Ollama, LM Studio, etc.), enabling provider-agnostic discovery, lifecycle management, hardware-fit checks, and delegated inference.
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Creative Commons Attribution Non Commercial No Derivatives 4.0 International
MCP server connecting Claude Code to LM Studio, delegating token-expensive tasks to a local model while keeping the cloud model in control. It reduces cloud context usage by reading files locally and returning only the processed results.
A fail-closed MCP server for Msty Studio on macOS that provides read-only diagnostics and an optional bounded local-generation tool, without accessing chats, keys, or configuration.
An MCP server that integrates with a local ComfyUI, allowing assistants to list and run workflows, monitor progress, and view results, as well as read and edit the live workflow canvas via an optional bridge node.
Routes one brief to the right image model across 60+ (gpt-image-1.5, Ideogram 3, Recraft V4, Flux), validates the output, and fans out to iOS/Android/PWA/favicon/visionOS/Flutter bundles. Works without an API key via Pollinations, HF Inference, Stable Horde, or host-LLM inline SVG.
Provides local vision and audio perception for MCP-compatible agents, enabling them to read images, transcribe text from visual media, analyze videos, and convert speech to text entirely on-device. It is privacy-focused with no cloud upload or API keys by default, using Ollama for inference.
Enables automatic collection and AI analysis of Slack messages to create organized Notion pages, supporting custom analysis directions like meeting minutes, issue extraction, and topic classification.
Delegates heavy, repetitive, and verifiable tasks like PDF extraction, code analysis, and log processing to a local LLM to reduce token consumption for frontier AI models, while keeping decision-making with the main AI.