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VictorForV

intervals-mcp

by VictorForV
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

get_training_readiness

Assess training readiness from CTL, ATL, ramp rate, and recent HRV. Get a coach-style TSB classification with risk bands, ramp-rate status, and HRV drop alerts.

Instructions

Assess current form from CTL/ATL/ramp rate and recent HRV, coach-style.

    Defaults to a six-week window so recent HRV has a real baseline to
    compare against. Returns TSB (fitness minus fatigue) classified into a
    band -- high_risk, overreaching, neutral, fresh, very_fresh -- each
    with a plain-language note, plus a ramp-rate read (declining,
    maintaining, building, aggressive) and, when HRV is logged, the last
    7 days against the rest of the window with a flag if it has dropped
    more than 10% below baseline.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
newestNo
oldestNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.4/5.0
Behavior5/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure and handles it well. It reveals the default six-week window, explains how TSB is classified, describes the ramp-rate read, and specifies the HRV threshold and comparison window. This gives an agent a strong mental model of what will happen when the tool is called.

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 detailed yet efficient, front-loading the core purpose and then elaborating on defaults and return values. Every sentence adds meaningful behavioral or output information without redundancy or fluff.

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 the absence of an output schema, the description thoroughly explains return values, classification bands, and conditional behavior. The main missing piece is explicit parameter semantics, but overall the description is complete enough for an agent to understand what the tool returns and how the default window behaves.

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?

Schema description coverage is 0%, so the description must compensate. It mentions the six-week default window, which implies that newest and oldest are optional date-range overrides, but it never explicitly explains those parameters or their expected format. This is minimally helpful but leaves a clear gap.

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 tool's purpose: assessing current training form from CTL/ATL/ramp rate and recent HRV. It goes beyond a simple verb+resource by specifying the computed metrics, output classification bands, and the HRV comparison logic, making it easy to distinguish from siblings like get_training_load_chart or get_wellness.

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

Usage Guidelines4/5

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

The description provides clear context for when to use the tool—when a coach-style readiness assessment based on training load and HRV is needed. It does not explicitly name alternatives or exclusion conditions, but the detailed output description makes the appropriate use case obvious.

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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