"Cloud Foundry" matching MCP servers:

An MCP server that allows AI assistants to interact with Foundry datasets, ontology objects, and functions through natural language queries and commands.

Enables interaction with Azure AI Foundry services including model exploration and deployment, knowledge management through AI Search, evaluation of text and agents, and fine-tuning operations. Provides unified access to Azure's AI capabilities through natural language commands.

Enables interaction with Azure AI Foundry services through a unified interface for model exploration and deployment, knowledge indexing and search, AI evaluation, and fine-tuning operations. Supports both GitHub token-based model testing and full Azure deployment workflows.

Enables interaction with Azure AI Foundry services for model exploration, deployment, and performance evaluation. It provides tools for managing knowledge bases via AI Search Service, executing fine-tuning jobs, and orchestrating AI agents through natural language.

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.

Enables AI assistants and other MCP clients to launch and steer Cursor Cloud Agents on repositories or as repo-less research tasks, including model selection, mid-run follow-ups, cancellation, result retrieval, and run listing. Also exposes authentication checks and per-agent or per-run token usage and cost reporting.

Enables AI agents to discover, evaluate, and provision cloud infrastructure across AWS, GCP, and Azure with cross-cloud normalization, cost comparisons, and deployable execution kits.

A Model Context Protocol (MCP) server that integrates Claude with the Terraform Cloud API, allowing Claude to manage your Terraform infrastructure through natural conversation.

Enables MCP-compatible AI agents to deploy applications to Google Cloud Run by providing tools for deploying code, listing services, and managing Google Cloud projects.

Enables management of Verda Cloud GPU instances through Claude, allowing users to deploy spot instances, attach volumes, and monitor status. It supports automated workflows for checking availability and configuring instances with startup scripts.

MCP server for SkyPilot that lets LLMs manage cloud clusters, jobs, and storage through the Model Context Protocol.

MCP Server for Google Cloud Healthcare API enables Agentic AI for a variety of FHIR-based digital health solutions, from smarter clinical workflows for Health Systems to Pre-Auth frameworks for Payers!

Enables deploying applications and managing cloud resources in Timeweb Cloud through natural language.

Enables AI assistants to interact with Google Cloud Platform resources through natural language queries. Supports querying and managing GCP services like Compute Engine, Cloud Storage, BigQuery, and more across multiple projects and regions.

Enables any MCP client to triage cloud agents, projects, conversations, transcripts, pull requests, and files, including listing agents, reading transcripts, following runs, and sending follow-ups, using an API key or browser sign-in.

Enables AI assistants to spawn and manage long-running cloud agents via MCP, offloading tasks to AWS Bedrock AgentCore.

Enables agents to deploy, run, expose, inspect, and manage isolated cloud environments, including databases, caches, browsers, GitHub-hosted apps, and arbitrary OCI images, via MCP tools.

A CLI and cloud platform providing managed hosting for MCP servers with intelligent tool filtering to reduce token costs and improve LLM accuracy. It serves as a secure proxy that connects AI clients to cloud tools while keeping sensitive credentials on the local machine.

Coordinates specialized agents (Architecture, Quality, Cloud, Prompt) to plan, build, test, and deploy applications with self-healing capabilities, authentication, and analytics for autonomous software engineering workflows.

Enables creating and running production-grade Model Context Protocol servers for REST APIs, GraphQL endpoints, OpenAPI specs, Google Cloud services, and custom Python logic from a declarative JSON blueprint, with built-in authentication, response shaping, multiple transports, and one-command Cloud Run deployment.