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Trainerize MCP Server

Trainerize MCP Server

by cotlet-connect
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

trainerize_type

Types text into a numbered field on the current Trainerize page, letting AI agents fill forms by field number from the last read.

Instructions

Type text into a numbered field on the current Trainerize page.

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": {
      "element": "<element>",
      "text": "<text>"
    },
    "name": "trainerize_type"
  }
}

Input Schema

NameRequiredDescriptionDefault
textYesText to type.
elementYesField number from the last read.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.0

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations, the description must carry the behavioral load; it discloses the target scoping (current page, field numbering from the prior read) but says nothing about whether existing text is replaced or appended, whether typing submits the form, or what happens if the field number is stale.

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?

A single front-loaded sentence with no filler or repetition; every word contributes to identifying the action and its target.

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?

For a simple two-parameter tool with no output schema, the core information is present, but the replace-vs-append behavior and any side effects on form state are unaddressed and matter for an agent driving a web page.

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 coverage is 100%, so both parameters are already documented; the description adds no format, length, or field-targeting nuance beyond what the schema provides, making the baseline 3 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 states a specific verb ('Type text') and target ('a numbered field on the current Trainerize page'), which cleanly separates it from siblings like trainerize_click or trainerize_read. It does not name or contrast a sibling explicitly, so it falls short of a 5.

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?

Usage is only implied: the 'numbered field from the last read' phrasing suggests you read a page first and then type, but the description never states that prerequisite or when to prefer this over trainerize_click.

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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