Run the full project onboarding sequence — checks and fixes all missing setup steps
AI agents invoke run_onboarding to trigger actions in Code Context. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call — builds kicked off, notifications sent, workflows started.
run_onboarding triggers real processes with real consequences. An agent gone sideways doesn't fire it once — it starts dozens of builds, sends mass notifications, or burns through compute before anyone looks up.
Attacks that exploit this kind of access
Run the full project onboarding sequence — checks and fixes all missing setup steps. It is categorised as a Execute tool in the Code Context MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Code Context MCP server in PolicyLayer and add a rule for run_onboarding: 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 Code Context. Nothing to install.
run_onboarding is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the run_onboarding 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 run_onboarding. 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.
run_onboarding is provided by the Code Context MCP server (velimirmueller/vlm-code-context-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.