AI agents use update_customer to create or update resources in Paystack — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Paystack environment.
Updating a customer modifies existing data reversibly (the record can be updated again). It does not delete, execute code, or move money, so Write is the most appropriate category. Misuse could expose or alter sensitive customer information, warranting medium severity.
From the tool's definition Tool name 'update_customer' and description 'Update a customer' indicate a modification operation on existing customer data.
Documented attack patterns abuse exactly the kind of access update_customer gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Paystack, and nothing reaches the server without passing your rules. This is the rule we recommend for update_customer:
{
"version": "1",
"default": "deny",
"tools": {
"update_customer": {
"limits": [
{
"counter": "update_customer_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}update_customer 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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Update a customer. It is categorised as a Write tool in the Paystack MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Paystack MCP server in PolicyLayer and add a rule for update_customer: 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 Paystack. Nothing to install.
update_customer 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 update_customer 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 update_customer. 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.
update_customer is provided by the Paystack MCP server (kohasummons/paystack-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Paystack, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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28 Paystack tools catalogued and risk-classified — across an index of 43,000+ MCP servers.