"Providing Mathematical and Scientific Calculation Functions for a Single-Modal LLM" matching MCP servers:

An MCP server for managing Modal — apps, containers, volumes, and secrets — and for deploying & running Modal apps directly from Claude Code and other MCP clients.

Exposes Modal cloud functions as MCP tools for video rendering, voice synthesis, and app management. Enables LLM agents to execute Remotion renders, F5-TTS voice cloning, and monitor Modal applications via Supabase-backed infrastructure.

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.

Local, read-only Modal context for coding agents. Enables agents to inspect Modal apps, deployments, logs, containers, volumes, and sandboxes without granting mutation tools.

Enables MCP-aware agents to drive Modal serverless compute, including GPU workers and headless Chromium for web browsing with screenshots.

Connects AI agents to Modal.com infrastructure, allowing management of deployments, apps, containers, volumes, secrets, and environments via MCP tools.

MCP server to manage and deploy Scaleway Serverless Functions, enabling creation, listing, updating, deleting, downloading, and logging of functions through natural language.

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 quickstart template to build and deploy a remote MCP server on Azure Functions using Python, secured with keys and HTTPS.

Enables AI assistants to interact with Databricks workspaces, running SQL queries, managing jobs, and exploring schemas via the Model Context Protocol.

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

Enables building and deploying custom tool, resource, prompt, and interactive weather app endpoints on serverless infrastructure, with local debugging, built-in authentication, API Management security, and network isolation.

Enables developers to run and deploy a custom remote MCP server on Azure Functions that exposes MCP tools, resources, and prompts—such as snippet CRUD, QR code generation, code review checklists, and interactive app UIs—with built-in Microsoft Entra authentication. Supports local debugging and one-command cloud deployment, with optional API Management and VNET network isolation.

Enables developers to build, run locally, and deploy to the cloud a custom MCP server on Azure Functions that exposes tools, resources, prompts, and an interactive weather app over HTTP. Includes built-in authentication via Microsoft Entra, plus options for API Management and VNET network isolation.

Enables developers to build, run locally with debugging, and deploy to the cloud custom remote MCP endpoints hosted on Azure Functions, with built-in Microsoft Entra authentication. Sample projects demonstrate snippet CRUD and QR code tools, resource templates, prompt workflows, and an interactive weather demo UI.

Publishes sample Python implementations that expose tools such as snippet CRUD, QR code generation and batch operations, resources, and prompts like code review checklists and doc generation, plus an interactive weather app, to MCP clients. These can be run locally with debugging or deployed to Azure with built-in Microsoft Entra authentication.

Enables developers to build and deploy custom tool, resource, and prompt integrations to the cloud using serverless functions, with local debugging and built-in authentication.

Hosts MCP tools for weather queries and On-Behalf-Of flow, enabling Copilot to retrieve weather data and user info via Azure Functions.

Enables exploring Microsoft Fabric / Power BI workspaces and semantic models, and executing ad-hoc DAX queries.