Create an approval request for a plan or specific steps.
AI agents use request_approval to create or update resources in Context Engine MCP Server — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Context Engine MCP Server environment.
This tool creates or initiates a new approval request artifact/record, which is a write operation that creates data. It is reversible (the approval request can be withdrawn, denied, or modified), and does not execute external code, delete data permanently, or move money.
From the tool's definition Tool creates an approval request ('Create an approval request'), which is a new data entry in a system with reversible side effects.
Documented attack patterns abuse exactly the kind of access request_approval gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Context Engine MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for request_approval:
{
"version": "1",
"default": "deny",
"tools": {
"request_approval": {
"limits": [
{
"counter": "request_approval_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
} request_approval stays usable, but capped — an agent stuck in a loop can't make hundreds of changes a minute. Everything else on the server is denied unless you say otherwise.
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Create an approval request for a plan or specific steps. It is categorised as a Write tool in the Context Engine MCP Server MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Context Engine MCP Server MCP server in PolicyLayer and add a rule for request_approval: 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 Context Engine MCP Server. Nothing to install.
request_approval is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the request_approval 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 request_approval. 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.
request_approval is provided by the Context Engine MCP Server MCP server (kirachon/context-engine). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Context Engine 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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50 Context Engine MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.