AI agents call sg_wages to retrieve information from Sgdata without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool queries publicly available Singapore government wage statistics. It performs a simple data retrieval with no side effects, no code execution, no financial transactions, and no destructive operations. The incomplete description ('overall') suggests it fetches aggregated earnings data. Classification as Read is appropriate for read-only statistical queries from government datasets.
From the tool's definition Tool retrieves 'Average monthly nominal earnings per employee in Singapore' — a data query operation with no modification, deletion, or execution capabilities.
Attacks that exploit this kind of access
Average monthly nominal earnings per employee in Singapore (overall. It is categorised as a Read tool in the Sgdata MCP Server, which means it retrieves data without modifying state.
Register the Sgdata MCP server in PolicyLayer and add a rule for sg_wages: 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 Sgdata. Nothing to install.
sg_wages 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 sg_wages 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 sg_wages. 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.
sg_wages is provided by the Sgdata MCP server (sypherin/sgdata-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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