crm_build_ai_workflow
Build and deploy a complete AI Workflow (Agent Studio agent) from a natural language description. Creates the agent, configures knowledge base connections, adds MCP server nodes for tool access, and promotes to production.
This record as markdown: /tools/io-github-0nork-0nmcp/crm-build-ai-workflow.md
What crm_build_ai_workflow does on 0nmcp
AI agents invoke crm_build_ai_workflow to trigger actions in 0nmcp. 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.
Why crm_build_ai_workflow is rated High
This tool executes a multi-step deployment workflow that creates an agent, configures knowledge base connections, adds MCP server nodes, and moves it to production. While it creates/modifies configuration (Write aspect), the deployment to production and agent activation constitute executable operations with external effects.
From the tool's definition Tool description explicitly states it "Build[s] and deploy[s] a complete AI Workflow" and "promotes to production." The verb 'deploy' and 'promotes to production' indicate triggering external operations whose effects depend on the natural language input…
Attacks that exploit this kind of access
The rule that runs crm_build_ai_workflow safely
PolicyLayer is an MCP gateway: it sits between your AI agents and 0nmcp, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For crm_build_ai_workflow, this is the rule to start with:
crm_build_ai_workflow 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.
The button opens the PolicyLayer dashboard: create your workspace, connect 0nmcp, apply this rule, and every crm_build_ai_workflow call is checked against it from then on.
Questions about crm_build_ai_workflow
Build and deploy a complete AI Workflow (Agent Studio agent) from a natural language description. Creates the agent, configures knowledge base connections, adds MCP server nodes for tool access, and promotes to production. It is categorised as a Execute tool in the 0nmcp MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the 0n MCP server in PolicyLayer and add a rule for crm_build_ai_workflow: 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 0nmcp. Nothing to install.
crm_build_ai_workflow 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 crm_build_ai_workflow 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 crm_build_ai_workflow. 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.
crm_build_ai_workflow is provided by the 0n MCP server (0nmcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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