AI agents invoke project_onboard to trigger actions in Workflows. 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.
The tool name 'project_onboard' suggests it triggers an onboarding workflow for a project, which likely involves multiple side effects such as creating resources, configuring settings, sending notifications, or provisioning services. In the context of a workflow automation server, this would be an Execute-level action. However, since the description is empty, confidence is lowered.
From the tool's definition Tool name 'project_onboard' on a server that 'Run YAML workflows as MCP tools so agents can automate real tasks'. Description is empty.
Documented attack patterns abuse exactly the kind of access project_onboard gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Workflows, and nothing reaches the server without passing your rules. This is the rule we recommend for project_onboard:
{
"version": "1",
"default": "deny",
"tools": {
"project_onboard": {
"limits": [
{
"counter": "project_onboard_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}project_onboard stays usable, but rate-capped — a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.
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project_onboard. It is categorised as a Execute tool in the Workflows MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Workflows MCP server in PolicyLayer and add a rule for project_onboard: 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 Workflows. Nothing to install.
project_onboard 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 project_onboard 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 project_onboard. 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.
project_onboard is provided by the Workflows MCP server (qtsone/workflows-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Workflows, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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13 Workflows tools catalogued and risk-classified — across an index of 43,000+ MCP servers.