openGym MCP Bridge

get_weekly_training_summary

Read-onlyIdempotent

Retrieve Monday-to-Sunday training trends for a date range, including working sets, volume, and effective sets by muscle group.

Instructions

Read Monday-week trends over explicit inclusive local dates (maximum 366 days). Working sets, volume, legacy primary/secondary credits and additive effective_sets using explicit muscle weights (zero means no load). Historical explicit weights precede current catalogue. Boundary weeks may be partial.

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": {
      "from": "<from>",
      "to": "<to>"
    },
    "name": "get_weekly_training_summary"
  }
}

Input Schema

Name	Required	Description	Default
`to`	Yes
`from`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.4.1Oct 27, 2026

TDQS

A3.7/5.0

Behavior4/5

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

Annotations already declare readOnly/idempotent/non-destructive/closed-world, so the bar is lower. The description still adds real behavioral context: boundary weeks may be partial, zero weight means no load, and historical explicit weights take precedence over the current catalogue. These are non-obvious data semantics an agent would otherwise miss.

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

Conciseness4/5

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

Content is front-loaded with the core purpose and the date range constraint first. It is dense and jargon-heavy but nearly every clause adds a constraint or data-semantics fact; only minor tightening would help.

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?

With no output schema, the description does mention what the summary contains (working sets, volume, credits, effective_sets) and flags partial boundary weeks. That covers return content reasonably, though the opaque terminology reduces practical 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?

Schema description coverage is 0%, so the description carries parameter meaning. It usefully specifies that from/to are inclusive local dates capped at 366 days, but never states the date format or clarifies which parameter is the start (schema only hints via maxLength 10). Partial compensation for the coverage gap.

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 opening sentence gives a specific verb and resource: 'Read Monday-week trends over explicit inclusive local dates (maximum 366 days).' It is clear what the tool returns, though it never names or distinguishes itself from siblings like get_summary or get_recent_workouts. The heavy domain jargon (legacy credits, effective_sets) slightly muddies the purpose.

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 supplies constraints (inclusive local dates, 366-day cap) that imply when it is applicable, but it never states when to prefer it over get_summary or get_recent_workouts. Usage is inferable rather than explicit.

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