"Modal" matching MCP servers:

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.

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

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.

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.

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.

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.

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 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.

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

This server facilitates the invocation of AI models from providers like Anthropic, OpenAI, and Groq, enabling users to manage and configure large language model interactions seamlessly.

A server that integrates trained Random Forest models with Bee Framework, enabling ReAct interactivity for AI tools and agents.

IMCP - Insecure Model Context Protocol The DVWA for AI Security! Welcome to IMCP – a deliberately vulnerable framework that exposes 16 critical security weaknesses in AI/ML systems. Whether you're a security researcher, developer, or educator, IMCP is your playground for hands-on learning about real

A lightweight TypeScript-based MCP server that demonstrates how to build custom MCP tools by implementing a simple addition calculator. Serves as a starting point for building MCP-compatible tools.

A template for deploying MCP servers on Vercel with serverless functions. Includes example tools for rolling dice and getting weather data to demonstrate basic functionality and API integration patterns.

This server lets tools-enabled LLMs work directly with SWI‑Prolog. It supports loading Prolog files, adding/removing facts and rules, listing symbols, and running queries with two modes: deterministic pagination and true engine backtracking.

An MCP server for integrating AI models with client applications, allowing for structured interactions with APIs using command-line arguments for authentication and configuration.

A drop-in MCP server implementation for Next.js projects using Vercel MCP Adapter, allowing developers to integrate model context protocol functionality with custom tools, prompts, and resources.

Enables the creation and deployment of API-based MCP tools for Cursor Agent using Cloudflare Workers, streamlining development and integration of custom functionalities.