machine_roles_doctor
Validate the saved runner/render split before dispatching work. Reads userSettings.machineRolesByProject, probes the configured AI runner and renderer with bounded transport checks, and returns a stable ready/code verdict so previews do not wait silently on an unreachable render box.
This record as markdown: /tools/io-github-kivanccakmak-yaver/machine-roles-doctor.md
What machine_roles_doctor does on Yaver
AI agents call machine_roles_doctor to retrieve information from Yaver without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
| Parameter | Type | Required | Description |
|---|---|---|---|
timeout_ms | integer | — | Per-candidate probe timeout. Default 4000. Runner and renderer checks are bounded. |
projectName | string | — | Optional project-scoped role row. Empty uses the account-wide favorite. |
Parameters from the server's own tool schema.
Why machine_roles_doctor is rated Low
This tool reads configuration and performs connectivity probes (health checks) against configured endpoints. It does not create, modify, delete, or execute any operations — it only validates and reports a status verdict. The probing involves bounded transport checks which are read-only diagnostics.
From the tool's definition Validate the saved runner/render split before dispatching work. Reads userSettings.machineRolesByProject, probes the configured AI runner and renderer with bounded transport checks, and returns a stable ready/code verdict
Attacks that exploit this kind of access
The rule that runs machine_roles_doctor 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_roles_doctor, this is the rule to start with:
machine_roles_doctor is read-only, so it stays allowed. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Yaver, apply this rule, and every machine_roles_doctor call is checked against it from then on.
Questions about machine_roles_doctor
Validate the saved runner/render split before dispatching work. Reads userSettings.machineRolesByProject, probes the configured AI runner and renderer with bounded transport checks, and returns a stable ready/code verdict so previews do not wait silently on an unreachable render box. It is categorised as a Read tool in the Yaver MCP Server, which means it retrieves data without modifying state.
machine_roles_doctor accepts 2 parameters: timeout_ms, projectName. 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_roles_doctor: 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_roles_doctor is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the machine_roles_doctor 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_roles_doctor. 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_roles_doctor 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.
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