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Trainzilla Coach MCP

by io.github.Trainzilla
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approve_ai_run

Approve an AI-generated plan so it is published to the client. Only after the coach has seen the plan. (confirm-gated: previews until confirm: true)

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": {
      "runId": "<runId>"
    },
    "name": "approve_ai_run"
  }
}

Input Schema

NameRequiredDescriptionDefault
runIdYes
confirmNoMust be true to actually execute. If false/omitted, returns a preview only.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4/5.0
Behavior4/5

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

Goes beyond the readOnlyHint=false annotation by disclosing the confirm-gated preview semantics and the visible side effect (plan becomes published to the client). It stops short of saying whether approval is reversible or how the plan can be unpublished.

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?

Three short clauses, with the core action and outcome front-loaded and the confirmation caveat parenthesized at the end. Nothing is wasted, though the second sentence is fragmentary.

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?

For a two-parameter mutation with an annotated safety profile and no output schema, the description covers the precondition, the side effect and the confirmation gate. The main remaining gap is where runId comes from and what the preview response contains.

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

Parameters2/5

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

Schema description coverage is 50%: confirm is documented in the schema, while runId has no description anywhere. The description only restates the confirm gating already in the schema and never explains what runId refers to (an ID from list_ai_runs_awaiting_review).

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?

States a specific verb (approve) plus the resource (an AI-generated plan) and the concrete consequence (published to the client). An agent can separate it from reject_ai_run and list_ai_runs_awaiting_review purely from the description.

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?

"Only after the coach has seen the plan" gives an explicit precondition for use. It doesn't name reject_ai_run as the alternative path or say what to do for un-reviewed runs, so it falls short of the 5 bar.

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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