Skip to main content
Glama
willc121

Garmin Health MCP Server

by willc121

get_training_load

Retrieve training load data with acute/chronic workload ratio to evaluate overtraining risk and optimize workout intensity.

Instructions

Get training load data including acute/chronic workload ratio to assess overtraining risk

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNoNumber of days (default: 30)

Implementation Reference

  • The handler function for the 'get_training_load' tool. Fetches the most recent training load data from the Supabase 'training_load' table, computes latest values and provides a history list.
    async function getTrainingLoad(days: number = 30) {
      const { data, error } = await supabase
        .from("training_load")
        .select("*")
        .order("calendar_date", { ascending: false })
        .limit(days);
    
      if (error) throw error;
    
      const latest = data?.[0];
      return {
        latest: {
          date: latest?.calendar_date,
          acute_load: latest?.acute_load,
          chronic_load: latest?.chronic_load,
          status: latest?.acwr_status,
        },
        history: data?.map((t) => ({
          date: t.calendar_date,
          acute: t.acute_load,
          chronic: t.chronic_load,
          status: t.acwr_status,
        })),
      };
    }
  • src/index.ts:380-390 (registration)
    Tool registration in the ListTools handler, including name, description, and input schema definition.
    {
      name: "get_training_load",
      description:
        "Get training load data including acute/chronic workload ratio to assess overtraining risk",
      inputSchema: {
        type: "object",
        properties: {
          days: { type: "number", description: "Number of days (default: 30)" },
        },
      },
    },
  • src/index.ts:423-425 (registration)
    Dispatch case in the CallToolRequestSchema handler that invokes the getTrainingLoad function.
    case "get_training_load":
      result = await getTrainingLoad(a.days || 30);
      break;
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves data (implying read-only behavior) but doesn't mention any behavioral traits such as authentication requirements, rate limits, data freshness, or error conditions. For a tool with no annotations, this leaves significant gaps in understanding how it operates.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, well-structured sentence that efficiently conveys the tool's purpose and key data included. It's front-loaded with the main action ('Get training load data') and adds necessary detail without redundancy. Every word earns its place, making it highly concise.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (retrieving calculated metrics), no annotations, no output schema, and 1 parameter with full schema coverage, the description is minimally adequate. It explains what data is fetched and its purpose, but lacks details on output format, behavioral constraints, or integration with sibling tools. This leaves room for improvement in completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, with the 'days' parameter documented as 'Number of days (default: 30)'. The description doesn't add any meaning beyond this, as it doesn't explain how the 'days' parameter affects the training load calculation or the acute/chronic ratio. With high schema coverage, the baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Get training load data' specifies the verb and resource, and 'including acute/chronic workload ratio to assess overtraining risk' adds valuable context about what specific data is retrieved and its purpose. However, it doesn't explicitly differentiate this tool from sibling tools like 'get_activities' or 'get_health_summary', which might also provide related fitness data.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It mentions assessing overtraining risk, which implies a use case, but doesn't specify prerequisites, compare it to sibling tools, or indicate when not to use it. For example, it doesn't clarify if this should be used instead of 'get_activities' for workload analysis.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/willc121/garmin-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server