"Understanding Modal Music" matching MCP servers:

An MCP server that enables AI agents to interact with Modal, allowing them to deploy apps and run functions in a serverless cloud environment.

An MCP server that allows users to deploy Python scripts to Modal directly from Claude, providing a link to the deployed application that can be shared with others.

A remote MCP bridge that lets MCP clients use Modal sandboxes for Linux execution, dependency installation, Git operations, GPU jobs, and invoking deployed Modal Functions.

MCP server that gives AI agents isolated, named Linux computers with stable IDs and lifecycle control. Supports multiple backends like Docker, Fly Machines, Modal, and E2B, with a dashboard for managing sandboxes.

A FastMCP server that turns JSON data into Plotly figures, deployable as an isolated service on Modal. Data goes in as JSON; a validated Plotly figure (JSON) or a rendered PNG comes back over the Model Context Protocol.

An MCP server implementing AI-user confirmation protocols, providing tools for LLMs to seek user confirmation when uncertain through yes/no questions, action confirmations, intent clarification, understanding verification, and satisfaction ratings.

MCP server for agentic interaction with the Lean theorem prover via LSP, providing tools for understanding, analyzing, and interacting with Lean projects.

Facilitates running Python code in a sandbox and generating images using the FLUX model via an MCP server compatible with clients like Goose and the Claude Desktop App.

Enables AI agents to control Ableton Live by evaluating arbitrary Python code and using predefined tools for common tasks, supporting voice and text commands for music production.

Provides AI assistants with deep semantic and runtime understanding of Jupyter notebooks.

Enables MCP-capable agents to author, compile, run, and commit generative music E-- Recipes from a library catalog, completing the full authoring loop.

An intelligent codebase processing server that provides agentic RAG capabilities for code repositories, enabling semantic search and contextual understanding through self-evaluating retrieval loops.

Enables coding agents to start and operate live kernels on local machines, Modal GPUs, or Colab VMs, running code, notebook cells, shell commands, and file transfers while inspecting variables, plots, and errors. It also runs notebooks, scripts, and web UIs end to end as budget-capped plans that verify expectations and report what failed and what changed.

A tiny public MCP server backed by an Envy-managed Modal sandbox that exposes a hello environment with standard sandbox tools like create_sandbox, bash, read, write, edit, and grep for ephemeral file and command execution.

A server that enables AI assistants like Claude to safely run Python code and access websites, processing data for better AI understanding while providing helpful error messages.

Enables AI agents to run arbitrary Python scripts on Modal's serverless infrastructure with on-demand CPU, memory, and GPU resources, providing scalable compute without maintaining always-on servers.

A TypeScript wrapper library for the Model Context Protocol SDK that provides a simplified interface for creating MCP servers with tools, resources, and prompts without needing to work directly with the protocol.

Runs a language server and provides tools for communicating with it. Language servers excel at tasks that LLMs often struggle with, such as precisely understanding types, understanding relationships, and providing accurate symbol references.

Enables AI assistants to build, test, and balance PICO-8 games through headless simulation, screenshots, and authoring of sprites, sound effects, and music.

An MCP server that queries multiple Ollama models and combines their responses, providing diverse AI perspectives on a single question for more comprehensive answers.