Perform an assignment action to submit completed work and progress workflow. This is the FINAL step after all required fields are filled. Auto-fetches eTag if not provided. Returns updated case with either nextAssignmentInfo (more work) or confirmationNote (workflow complete). Local actions stay ...
AI agents invoke perform_assignment_action to trigger actions in Pega DX MCP Server. 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 tool triggers workflow progression in Pega Platform — it submits work and advances business processes. This is an Execute action because it runs external operations (workflow transitions, assignment completions) whose effects depend on the arguments passed.
From the tool's definition 'Perform an assignment action to submit completed work and progress workflow' and 'Returns updated case with either nextAssignmentInfo (more work) or confirmationNote (workflow complete)'
Documented attack patterns abuse exactly the kind of access perform_assignment_action gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Pega DX MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for perform_assignment_action:
{
"version": "1",
"default": "deny",
"tools": {
"perform_assignment_action": {
"limits": [
{
"counter": "perform_assignment_action_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}perform_assignment_action 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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Perform an assignment action to submit completed work and progress workflow. This is the FINAL step after all required fields are filled. Auto-fetches eTag if not provided. Returns updated case with either nextAssignmentInfo (more work) or confirmationNote (workflow complete). Local actions stay at current assignment; connector actions progress to next assignment. It is categorised as a Execute tool in the Pega DX MCP Server MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Pega DX MCP Server MCP server in PolicyLayer and add a rule for perform_assignment_action: 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 Pega DX MCP Server. Nothing to install.
perform_assignment_action 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 perform_assignment_action 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 perform_assignment_action. 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.
perform_assignment_action is provided by the Pega DX MCP Server MCP server (marco-looy/pega-dx-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Pega DX MCP Server, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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65 Pega DX MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.