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.
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.
An AI agent can call machine_onboarding_apply faster than any human can review — one bad instruction and it creates or modifies resources in Yaver by the hundred, each call as confident as the last.
Attacks that exploit this kind of access
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.
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.
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.
machine_onboarding_apply is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
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.
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.
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.