AI agents call list_payments to retrieve information from Paymongo without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool queries and retrieves payment records using pagination parameters. It performs a read-only operation that does not create, modify, delete, or execute any code. The cursor-based pagination mechanism (before/after) confirms it is a standard list/query operation.
From the tool's definition Tool name is 'list_payments' and description states it 'List payments with cursor pagination (before/after by payment ID)' — a retrieval operation with no side effects.
Attacks that exploit this kind of access
List payments with cursor pagination (before/after by payment ID). It is categorised as a Read tool in the Paymongo MCP Server, which means it retrieves data without modifying state.
Register the Paymongo MCP server in PolicyLayer and add a rule for list_payments: 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 Paymongo. Nothing to install.
list_payments is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the list_payments 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 list_payments. 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.
list_payments is provided by the Paymongo MCP server (theyahia/paymongo-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
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