Query your cloud with natural language: A developer’s guide to Gcore MCP

What if you could ask your infrastructure questions and get real answers?

With Gcore’s open-source implementation of the Model Context Protocol (MCP), now you can. MCP turns generative AI into an agent that understands your infrastructure, responds to your queries, and takes action when you need it to.

In this post, we’ll demo how to use MCP to explore and inspect your Gcore environment just by prompting, to list resources, check audit logs, and generate cost reports. We’ll also walk through a fun bonus use case: provisioning infrastructure and exporting it to Terraform.

What is MCP and why do devs love it?

Originally developed by Anthropic, the Model Context Protocol (MCP) is an open standard that turns language models into agents that interact with structured tools: APIs, CLIs, or internal systems. Gcore’s implementation makes this protocol real for our customers.

With MCP, you can:

• Ask questions about your infrastructure
• List, inspect, or filter cloud resources
• View cost data, audit logs, or deployment metadata
• Export configs to Terraform
• Chain multi-step operations via natural language

Gcore MCP removes friction from interacting with your infrastructure. Instead of wiring together scripts or context-switching across dashboards and CLIs, you can just…ask.

That means:

• Faster debugging and audits
• More accessible infra visibility
• Fewer repetitive setup tasks
• Better team collaboration

Because it’s open source, backed by the Gcore Python SDK, you can plug it into other APIs, extend tool definitions, or even create internal agents tailored to your stack. Explore the GitHub repo for yourself.

What can you do with it?

This isn’t just a cute chatbot. Gcore MCP connects your cloud to real-time insights. Here are some practical prompts you can use right away.

Infrastructure inspection

• “List all VMs running in the Frankfurt region”
• “Which projects have over 80% GPU utilization?”
• “Show all volumes not attached to any instance”

Audit and cost analysis

• “Get me the API usage for the last 24 hours”
• “Which users deployed resources in the last 7 days?”
• “Give a cost breakdown by region for this month”

Security and governance

• “Show me firewall rules with open ports”
• “List all active API tokens and their scopes”

Experimental automation

• “Create a secure network in Tokyo, export to Terraform, then delete it”

We’ll walk through that last one in the full demo below.

Full video demo

Watch Gcore’s AI Software Engineer, Algis Dumbris, walk through setting up MCP on your machine and show off some use cases. If you prefer reading, we’ve broken down the process step-by-step below.

Step-by-step walkthrough

This section maps to the video and shows exactly how to replicate the workflow locally.

1. Install MCP locally (0:00–1:28)
We use uv to isolate the environment and pull the project directly from GitHub.

curl -Ls https://astral.sh/uv/install.sh | sh
uvx add gcore-mcp-server https://github.com/G-Core/gcore-mcp-server

Requirements:

• Python
• Gcore account + API key
• Tool config file (from the repo)

2. Set up your environment (1:28–2:47)
Configure two environment variables:
GCORE_API_KEY for auth
GCORE_TOOLS to define what the agent can access (e.g., regions, instances, costs, etc.)

Soon, tool selection will be automatic, but today you can define your toolset in YAML or JSON.

3. Run a basic query (3:19–4:11)
Prompt:
“Find the Gcore region closest to Antalya.”

The agent maps this to a regions.list call and returns: Istanbul
No need to dig through docs or write an API request.

4. Provision, export, and clean up (4:19–5:32)
This one’s powerful if you’re experimenting with CI/CD or infrastructure-as-code.

Prompt:
“Create a secure network in Tokyo. Export to Terraform. Then clean up.”

The agent:

• Provisions the network
• Exports it to Terraform format
• Destroys the resources afterward

You get usable .tf output with no manual scripting. Perfect for testing, prototyping, or onboarding.

Gcore: always building for developers

Try it now:

• Clone the repo
• Install UVX + configure your environment
• Start prompting your infrastructure
• Open issues, contribute tools, or share your use cases

This is early-stage software, and we’re just getting started. Expect more tools, better UX, and deeper integrations soon.

Watch how easy it is to deploy an inference instance with Gcore