"Elastic Cloud" matching MCP servers:

Generates, validates, and manages Cloud Custodian policies across AWS, GCP, and Azure using natural language prompts.

Connects to Elastic Agent Builder to enable AI-powered application health monitoring using OTEL metrics, traces, and logs from Elastic Serverless Observability.

Multi-cloud MCP server that exposes cloud AI models as tools for AI CLI agents, supporting streaming, conversation history, parallel multi-model queries, and dynamic model discovery.

Enables MCP-compatible AI clients to retrieve FinOps-aligned knowledge and runbooks on demand, covering cloud cost optimization across AWS, Azure, GCP, and OCI, AI inference economics, Kubernetes, data platforms, allocation, chargeback, anomaly management, and waste detection.

Hardened MCP bridge to the full Ollama ecosystem — local and cloud models. 6 tools covering health, model management, chat, and generation with SSRF mitigation, singleton HTTP client, and structured error handling. Two deps, one file.

AI-powered cloud architecture - describe infrastructure in natural language, get Terraform, cost estimates, and compliance reports

Provides vision capabilities to text-only LLMs by acting as a cloud vision adapter layer, enabling image understanding and OCR text extraction through a single MCP tool.

mediamcp is an MCP server that connects your AI assistant — Claude Code, Claude Desktop, Cursor, Windsurf, VS Code, or anything else that speaks MCP — to cloud media models (Gemini Flash Image, GPT-5 Image, Seedream, Veo, Sora, …) via OpenRouter or any OpenAI-compatible API.

Evaluates task suitability for local models before cloud API calls, routing to Ollama or similar to reduce costs.

Atlas Cloud MCP Server enables developers to access state-of-the-art AI models for image generation, video generation, and LLMs through a unified MCP interface. Fast, scalable, and cost-efficient alternative to traditional AI providers.

Routes questions to a council of AI models (local and cloud) and synthesizes their answers in five configurable modes: individual, categorized, deconflicted, pooled, and dialectic.

Local pseudonymisation MCP server that detects PII in text, replaces it with opaque tokens before sending to cloud LLMs, and restores tokens afterward.

Enables AI agents to analyze images via user-configured cloud vision APIs (Gemini or OpenAI-compatible), returning structured results such as summaries, OCR text, and objects.

Provides a sequentialthinking tool for dynamic, reflective problem-solving via chain-of-thought reasoning. Supports local Stdio and remote SSE deployment on Google Cloud Run.

Enables safe interaction with cloud LLMs by redacting sensitive entities into reversible placeholders, enforcing deterministic egress policies with human approval, and rehydrating responses so real data never leaves the process.

Connects AI assistants to Cloudglue for video analysis, enabling video description, entity extraction, chapter segmentation, and metadata retrieval from various sources like YouTube and cloud storage.

MCP server that enables cloud models (like Gemini/Claude) to delegate coding tasks to a local llama.cpp server, preserving cloud usage limits through an AI-powered code review loop.

Enables cloud-based MCP clients to delegate privacy-sensitive or large-output tasks to a local LLM, returning sanitized placeholders or digested summaries so private data never leaves the user's network.

MCP server for 2DAI that enables AI assistants to generate images and videos, upload references, browse the public feed, and manage cloud drive on a 2DAI account.

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.