"A tool or platform for assisting in AI development" matching MCP servers:

A read-only MCP server that enables AI agents to act as GCP platform engineers, allowing them to investigate incidents, take inventory, and find cost-optimization opportunities in Google Cloud projects without mutating any infrastructure.

Read-only MCP server that exposes Kubernetes platform state (tenants, pods, SLOs, ArgoCD applications, chaos schedules, and catalog services) to AI agents, enabling natural language queries about cluster health and configuration.

Enables AI agents to interact with ServiceNow through MCP, providing schema inspection, record CRUD, attachments, audit, Flow Designer, and platform investigations with policy-guarded access.

Enables DevOps operators to query and operate the Sigma Streaming Platform from any AI client such as Claude Desktop, Cursor, or Kiro.

Enables users to describe their LLM fine-tuning job once and get the cheapest, fastest, and most balanced GPU options across a dozen cloud providers in seconds.

Enables platform engineering workflows via GitHub Copilot, including creating repositories from templates and provisioning Azure deployment environments.

Enables users to describe an LLM fine-tuning workload and receive ranked, cost- and runtime-optimized GPU recommendations across multiple cloud providers by estimating VRAM and FLOPs.

Enables users to describe an LLM fine-tuning job and receive the cheapest, fastest, or most balanced GPU options across multiple cloud providers by calculating required VRAM and FLOPs.

Enables users to describe an LLM fine-tuning job and receive ranked GPU recommendations across a dozen cloud providers based on estimated VRAM/FLOPs, live pricing, cost, and speed. It returns cheapest, fastest, and balanced hardware options without manual comparison.

It enables users to describe an LLM fine-tuning workload once and receive task-aware VRAM/FLOPs estimates plus ranked cheapest, fastest, or balanced GPU recommendations across multiple cloud providers.

Enables users to describe an LLM fine-tuning job once and receive ranked GPU recommendations across many cloud providers based on estimated VRAM/FLOPs, cost, and runtime.

Enables users to describe an LLM fine-tuning job in natural language and receive ranked GPU recommendations from a dozen cloud providers, based on estimated VRAM and FLOPs requirements. It surfaces the cheapest, fastest, and most balanced hardware options while disclosing the source and freshness of each price.

Enables users to describe an LLM fine-tuning job once and get the cheapest, fastest, or most balanced GPU options across multiple cloud providers by estimating required VRAM and FLOPs.

Enables users to describe an LLM fine-tuning workload once and get task-aware VRAM/FLOPs calculations with cheapest, fastest, and balanced GPU recommendations across a dozen cloud providers. It supports CLI, REST API, and natural-language MCP integration without manual price comparison.

MCP server giving AI assistants read-only access to a Jamf fleet via the Jamf Platform API Gateway using OAuth2 client credentials, with tools for fleet overview, device queries, group membership, automation auditing, and passthrough requests.

MCP server for Azure resource management, AI Foundry, and Entra ID — inspect and operate Azure infrastructure through AI agents.

Enables interaction with Mia-Platform Console through the Model Context Protocol, supporting machine-to-machine authentication and personal credentials for managing resources.

Connects to a GCP-hosted AOSP build VM via IAP tunnel, uploads APKs as privileged system apps, and builds system.img.

Enables intelligent management of Google Cloud Platform infrastructure using Terragrunt, with support for experimental stacks, parallel deployments, resource visualization, cost analysis, and comprehensive infrastructure operations through natural language.

Enables MCP clients to fetch the signed-in Microsoft Entra user's profile from Microsoft Graph via the GET /me endpoint using the provided bearer token.