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strength-training-mcp

strength-training-mcp

by daiduo2
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

calculate_fatigue_score

Compute Banister fitness-fatigue metrics (CTL, ATL, TSB) from training sets to quantify chronic and acute training load. Requires 14+ days of data, 42 days recommended for full accuracy.

Instructions

Calculate Banister fitness-fatigue metrics (CTL/ATL/TSB) from recent training sets. Returns MISSING_BASELINE if sets is empty (constraint C4). Provide at least 14 days for partial CTL/ATL, 42 days recommended for full.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "sets": "<sets>"
    },
    "name": "calculate_fatigue_score"
  }
}

Input Schema

NameRequiredDescriptionDefault
setsYesRecent training sets. Provide ≥42 days for full CTL/ATL accuracy.
recoveryNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

A4/5.0
Behavior3/5

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

With no annotations, the description carries full burden. It reveals the tool returns 'MISSING_BASELINE' if sets are empty, implying a constraint. It does not discuss read-only nature, side effects, auth needs, or output format, but the behavior is partially disclosed.

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?

Two sentences achieve high information density: first sentence states purpose, second sentence adds usage guidelines and error constraint. No superfluous content, front-loaded.

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

Completeness4/5

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

Given no output schema, the description lacks details about the return format (e.g., CTL, ATL, TSB values). It covers input requirements and one error scenario, but for a calculation tool, output description would enhance completeness. Still fairly complete for its complexity.

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

Parameters4/5

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

The description adds context to the 'sets' parameter by specifying recommended day counts (14/42), which goes beyond the schema's brief 'Recent training sets. Provide ≥42 days for full CTL/ATL accuracy.' The 'recovery' parameter has no schema description but the tool description does not mention it. Overall, value added.

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

Purpose5/5

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

The description clearly states the verb 'Calculate' and the resource 'Banister fitness-fatigue metrics (CTL/ATL/TSB) from recent training sets'. It is distinct from sibling tools like 'suggest_session_modification' or 'recommend_session_for_today', which have different purposes.

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

Usage Guidelines3/5

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

The description provides implicit usage guidance by recommending data lengths ('Provide at least 14 days for partial CTA/L, 42 days recommended for full') and noting a specific error condition for empty input. However, it does not explicitly compare with siblings or state when not to use this tool.

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

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