machine_onboarding_apply

SERVERYaver SOURCEyaver-cli

Medium RISK CLASS

Category Write

Parameters 90 required

Recommended Rate-limitedsee the rule below

Registry record Grade F, identity unverified Pull the record →

This record as markdown: /tools/io-github-kivanccakmak-yaver/machine-onboarding-apply.md

WHAT IT DOES

What machine_onboarding_apply does on Yaver

AI agents use machine_onboarding_apply to create or update resources in Yaver, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Yaver environment.

Parameter	Type	Required	Description
`notes`	string	—
`device_id`	string	—	Optional remote device ID
`device_ids`	array	—	Optional list of owned remote device IDs
`apply_clone`	boolean	—	Write clone/pull credentials (default true)
`gitlab_host`	string	—	Defaults to gitlab.com
`github_token`	string	—
`gitlab_token`	string	—
`apply_ci_token`	boolean	—	Write CI/deploy vault token (default true)
`openai_api_key`	string	—

Parameters from the server's own tool schema.

RISK

Why machine_onboarding_apply is rated Medium

This tool creates or modifies sensitive configuration data (credentials, tokens, authentication credentials) on local or remote machines. While it doesn't delete data (Destructive) or move money (Financial), and doesn't execute arbitrary code (Execute), it does persistently write authentication credentials and CI/deploy tokens to storage.

From the tool's definition Tool description explicitly states it 'Stores OpenAI in vault' and 'can write clone credentials plus CI/deploy tokens', which are Write operations that modify local/machine configuration and credential storage.

Attacks that exploit this kind of access

RECOMMENDED RULE

The rule that runs machine_onboarding_apply safely

PolicyLayer is an MCP gateway: it sits between your AI agents and Yaver, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For machine_onboarding_apply, this is the rule to start with:

machine_onboarding_apply Rate-limited

machine_onboarding_apply stays usable, but capped: an agent stuck in a loop can't make hundreds of changes a minute. Everything else on the server is denied unless you say otherwise.

View as policy code

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "machine_onboarding_apply": {
      "limits": [
        {
          "counter": "machine_onboarding_apply_rate",
          "window": "minute",
          "max": 30,
          "scope": "grant"
        }
      ]
    }
  }
}

LIMIT THIS TOOL → Instant setup, no code required.

The button opens the PolicyLayer dashboard: create your workspace, connect Yaver, apply this rule, and every machine_onboarding_apply call is checked against it from then on.

FAQ

Questions about machine_onboarding_apply

What does the machine_onboarding_apply tool do? +

Configure OpenAI, GitHub, and GitLab onboarding on the local machine or on one or more owned Yaver machines. Stores OpenAI in vault, and for GitHub/GitLab can write clone credentials plus CI/deploy tokens. It is categorised as a Write tool in the Yaver MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

What parameters does machine_onboarding_apply accept? +

machine_onboarding_apply accepts 9 parameters: notes, device_id, device_ids, apply_clone, gitlab_host, github_token, gitlab_token, apply_ci_token, openai_api_key. The full parameter table on this page comes from the server's own tool schema.

How do I enforce a policy on machine_onboarding_apply? +

Register the Yaver MCP server in PolicyLayer and add a rule for machine_onboarding_apply: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Yaver. Nothing to install.

What risk level is machine_onboarding_apply? +

machine_onboarding_apply is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.

Can I rate-limit machine_onboarding_apply? +

Yes. Add a rate_limit block to the machine_onboarding_apply rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.

How do I block machine_onboarding_apply completely? +

Set action: deny in the PolicyLayer policy for machine_onboarding_apply. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.

What MCP server provides machine_onboarding_apply? +

machine_onboarding_apply is provided by the Yaver MCP server (yaver-cli). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

KEEP EXPLORING

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