Primary AI orchestration interface - intelligently processes complex requests by automatically selecting and coordinating multiple tools. Handles file operations, git management, web search, web fetching, browser automation, security analysis, and more. Describe your goal naturally - the AI will ...
AI agents invoke ai_process to trigger actions in Orchestrator MCP. 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.
This is a meta-orchestration tool that automatically selects and runs other tools based on natural language input. It combines file operations, git commands, browser automation, and security analysis—all Execute-category actions. The AI-driven selection mechanism means the exact operations cannot be predetermined, making this inherently an Execute tool.
From the tool's definition Tool description explicitly states it 'automatically selecting and coordinating multiple tools' including 'file operations, git management, web search, web fetching, browser automation, security analysis, and more' with ability to 'execute multi-step…
Documented attack patterns abuse exactly the kind of access ai_process gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Orchestrator MCP, and nothing reaches the server without passing your rules. This is the rule we recommend for ai_process:
{
"version": "1",
"default": "deny",
"tools": {
"ai_process": {
"limits": [
{
"counter": "ai_process_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}ai_process 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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Primary AI orchestration interface - intelligently processes complex requests by automatically selecting and coordinating multiple tools. Handles file operations, git management, web search, web fetching, browser automation, security analysis, and more. Describe your goal naturally - the AI will determine the best approach and execute multi-step workflows. It is categorised as a Execute tool in the Orchestrator MCP MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Orchestrator MCP server in PolicyLayer and add a rule for ai_process: 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 Orchestrator MCP. Nothing to install.
ai_process 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 ai_process 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 ai_process. 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.
ai_process is provided by the Orchestrator MCP server (phoenixrr2113/orchestrator-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Orchestrator MCP, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
Free to start. No card required.
3 Orchestrator MCP tools catalogued and risk-classified — across an index of 43,000+ MCP servers.