Get training effect data for a specific activity Args: activity_id: ID of the activity to retrieve training effect for
AI agents call get_training_effect to retrieve information from Garmin MCP Server without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool retrieves existing fitness data (training effect metrics) from Garmin Connect without creating, modifying, deleting, or executing any operations. It is a pure read operation with minimal blast radius—misuse would only expose personal health data already owned by the user, with no side effects or irreversible changes possible.
From the tool's definition Tool name 'get_training_effect' and description 'Get training effect data for a specific activity' indicate a retrieval operation with no modification capabilities. The argument is a simple activity_id lookup parameter.
Documented attack patterns abuse exactly the kind of access get_training_effect gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Garmin MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for get_training_effect:
{
"version": "1",
"default": "deny",
"tools": {
"get_training_effect": {}
}
}get_training_effect is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
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Get training effect data for a specific activity Args: activity_id: ID of the activity to retrieve training effect for. It is categorised as a Read tool in the Garmin MCP Server MCP Server, which means it retrieves data without modifying state.
Register the Garmin MCP Server MCP server in PolicyLayer and add a rule for get_training_effect: 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 Garmin MCP Server. Nothing to install.
get_training_effect 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 get_training_effect 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 get_training_effect. 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.
get_training_effect is provided by the Garmin MCP Server MCP server (taxuspt/garmin_mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 126 Garmin MCP Server tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
Free to start. No card required.
126 Garmin MCP Server tools catalogued and risk-classified — across an index of 42,500+ MCP servers.