# Use IONOS CLOUD Resources in Agentic Workflows

**Who this is for:** Platform engineers and developers building automated pipelines, CI/CD workflows, or AI agent systems that need real-time access to <code class="expression">space.vars.ionos\_cloud</code> infrastructure data.

AI agents can use the MCP server as part of multi-step [<mark style="color:blue;">agentic workflows</mark>](https://docs.ionos.com/cloud/support/general-information/glossary-of-terms#agentic-workflow). The read-only tools are well suited for discovery, validation, and reporting tasks.

## Infrastructure discovery

An agent can enumerate your entire <code class="expression">space.vars.ionos\_cloud</code> environment as part of a larger task:

> Scan all my data centers, list every server and its attached volumes, and produce a CSV summary.

This uses `list_datacenters`, `list_servers`, and `list_server_volumes` in sequence.

## Pre-deployment validation

Before running [<mark style="color:blue;">Terraform</mark>](https://www.terraform.io) or another provisioning tool, use the MCP server to validate the target environment:

> Check whether the LAN `internal-net` exists in data center `prod-fra` and confirm there are no conflicting firewall rules for port 443.

## Cost reporting

Use Billing and Compute tools together for cost attribution reports:

> List all servers across all data centers, group them by location, and pull the usage data for each. Summarise the estimated monthly cost per location.

## Cloud DNS audit

> List all DNS zones and their records. Flag any zones without SOA records or with more than 500 records.

## Certificate expiry checks

> List all certificates in Certificate Manager and flag any that expire within 30 days.

## Unattended compliance audits (scheduled agent)

Because every tool is read-only, the MCP server is safe to deploy inside an unattended agent loop running on a CI scheduler, cron, or a long-running agent worker. The agent cannot break anything; the worst case is a noisy alert.

A common pattern: every night the agent runs a four-step loop without a human in the chat.

1. Enumerate the resources to audit (for example, `list_object_storage_buckets` across every region).
2. Run the relevant inspection tools on each (for Object Storage: the six security-posture probes such as `get_object_storage_bucket_public_access_block` and `get_object_storage_bucket_encryption`).
3. Compare the result with last night's snapshot stored in a Git repo or an object store.
4. If anything regressed (new public access, encryption disabled, lifecycle removed), open a ticket in your issue tracker with the affected resource, the regression, and a diff against the prior known-good state.

The same agent can run on Claude Code with a project-scoped `.mcp.json`, on a custom runner built with the [<mark style="color:blue;">OpenAI Agents SDK</mark>](https://openai.github.io/openai-agents-python/) or [<mark style="color:blue;">LangGraph</mark>](https://langchain-ai.github.io/langgraph/), or on any other agent framework that speaks MCP. The server is identical in all cases; only the orchestration framework changes.

{% hint style="info" %}
**Rate limits on unattended agents:** A nightly audit over a large account can issue hundreds of sequential API calls. If you hit HTTP 429 responses, add short pauses between tool calls and reduce per-step parallelism. For more information, see [<mark style="color:blue;">Troubleshooting</mark>](/cloud/ai/mcp-server/troubleshooting.md#rate-limit-errors).
{% endhint %}

## Composing multiple MCP servers

The MCP server runs alongside other MCP servers in the same AI client. The AI client merges all tool catalogs, so a single prompt can draw on multiple data sources.

### With the IONOS CLOUD Documentation MCP

The <code class="expression">space.vars.ionos\_cloud</code> documentation site exposes its own MCP server at `https://docs.ionos.com/cloud/~gitbook/mcp`: a GitBook-powered server with documentation search and retrieval tools. Adding it alongside the infrastructure MCP gives the AI assistant both live data and product knowledge in the same session. **Both servers are completely independent; you can use either one without the other.**

{% hint style="info" %}
**Context window note:** Adding a second MCP server increases your total tool catalog. If you use Windsurf (100-tool limit) or another client with a tool cap, set `IONOS_MCP_LOAD_MODE=lazy` on the IONOS server so only the always-on tools and loader sentinels are registered up front. For more information, see [<mark style="color:blue;">Selective Tool Loading</mark>](/cloud/ai/mcp-server/configuration/selective-tool-loading.md).
{% endhint %}

```json
{
  "mcpServers": {
    "ionoscloud-docs": {
      "url": "https://docs.ionos.com/cloud/~gitbook/mcp"
    },
    "ionoscloud": {
      "command": "/usr/local/bin/ionoscloud-mcp",
      "env": {
        "IONOS_TOKEN": "your-api-token"
      }
    }
  }
}
```

Example prompts that use both servers together:

> Look up how FOCUS v1.3 billing works in the <code class="expression">space.vars.ionos\_cloud</code> docs, then pull my actual billing data for this month and produce a FOCUS-compliant summary.

> According to the <code class="expression">space.vars.ionos\_cloud</code> documentation, what is the recommended token scope for Object Storage? Then check my actual account configuration and report whether it matches.

> Read the <code class="expression">space.vars.ionos\_cloud</code> Certificate Manager documentation, then list all my certificates and flag any that expire within the timeframes the docs recommend monitoring.

> What changed in the latest <code class="expression">space.vars.ionos\_cloud</code> release? Then check whether any of those products are in use in my account.

### With other tools

To build a workflow that reads IONOS infrastructure data and then commits a report to GitHub, configure both servers in the same `mcpServers` block:

```json
{
  "mcpServers": {
    "ionoscloud": {
      "command": "/usr/local/bin/ionoscloud-mcp",
      "env": {
        "IONOS_TOKEN": "your-api-token"
      }
    },
    "github": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-github"],
      "env": {
        "GITHUB_PERSONAL_ACCESS_TOKEN": "your-github-token"
      }
    }
  }
}
```

You can then ask: *"List all servers in my production data center and create a GitHub issue summarising any that have no attached volumes."*

## Combining with other tools

The MCP server integrates naturally with other tools your AI client has access to. Combine it with a file write tool to export infrastructure data to a spreadsheet, or with a notification tool to send alerts when checks fail.

{% hint style="info" %}
**Keep the default eager mode for agent workflows on clients without a tool cap.** The MCP server registers all tools at startup by default, which is the correct setting for unattended agents on Claude Code, Claude Desktop, Cursor, VS Code, and similar. Do **not** set `IONOS_MCP_LOAD_MODE=lazy` on these clients: in lazy mode, Compute Engine and Object Storage tools only register after an explicit loader-sentinel call, and an agent running a multi-step task will not know to issue that call. Silent failure looks like this: the agent says "I don't have a tool to list your servers" instead of returning an error. To defend against an inherited `IONOS_MCP_LOAD_MODE` env value from the host shell of a CI runner, pin `"IONOS_MCP_LOAD_MODE": "eager"` explicitly in the agent's MCP config.

**Windsurf is the exception.** Windsurf's 100-tool cap takes precedence and lazy mode is the only option. On Windsurf, expect to prompt the agent explicitly with *"Load the Compute tools"* (or *"Load the Object Storage tools"*) before any step that needs those tools — the agent loop must include that priming step.
{% endhint %}


---

# Agent Instructions
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