AI agents call list_traces to retrieve information from Maple without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool retrieves and lists historical trace data and associated risk metadata without modifying, deleting, or executing any operations. It is a passive observability/auditing read operation consistent with Maple's role as a monitoring and auditing layer. The low severity reflects that retrieving logs and metadata poses minimal risk of misuse by an agent.
From the tool's definition Tool name 'list_traces' and description 'List recent traces and risk metadata' indicate a query/retrieval operation with no side effects.
Attacks that exploit this kind of access
List recent traces and risk metadata. It is categorised as a Read tool in the Maple MCP Server, which means it retrieves data without modifying state.
Register the Maple MCP server in PolicyLayer and add a rule for list_traces: 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 Maple. Nothing to install.
list_traces 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_traces 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_traces. 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_traces is provided by the Maple MCP server (omar2001ramadan/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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