Get comments associated with a specific workout by its workout ID.
AI agents call tp_get_workout_comments to retrieve information from TrainingPeaks MCP Server 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 existing data (workout comments) without modifying, executing, deleting, or creating anything. It is a straightforward read-only operation with minimal risk even if an AI agent misuses it, as the worst case is viewing unintended comments within the user's own TrainingPeaks account.
From the tool's definition Tool name 'tp_get_workout_comments' and description explicitly state it 'Get comments associated with a specific workout' — a retrieval operation with no side effects.
Attacks that exploit this kind of access
Get comments associated with a specific workout by its workout ID. It is categorised as a Read tool in the TrainingPeaks MCP Server MCP Server, which means it retrieves data without modifying state.
Register the TrainingPeaks MCP Server MCP server in PolicyLayer and add a rule for tp_get_workout_comments: 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 TrainingPeaks MCP Server. Nothing to install.
tp_get_workout_comments 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 tp_get_workout_comments 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 tp_get_workout_comments. 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.
tp_get_workout_comments is provided by the TrainingPeaks MCP Server MCP server (tildecomunicacion/trainingpeaks_mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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