TextToolkit

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case_to_train

Transform text into Train-Case, capitalizing each word and joining them with hyphens. Useful for standardizing titles or identifiers.

Instructions

Convert text to Train-Case

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": {
      "text": "<text>"
    },
    "name": "case_to_train"
  }
}

Input Schema

Name	Required	Description
`text`	Yes	The text to transform to Train-Case
`delimiter`	No	The character to use between words (optional)
`locale`	No	Locale for case conversion (optional)
`mergeAmbiguousCharacters`	No	Whether to merge ambiguous characters (optional)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Jun 21, 2026

TDQS

C2.9/5.0

Behavior2/5

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

No annotations provided; the description does not disclose behavioral aspects like handling of special characters, edge cases, or output format.

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?

Single sentence, concise, and front-loaded with the core action.

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

Completeness2/5

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

Lacks explanation of what Train-Case is and how it differs from other case styles; incomplete for an agent to choose effectively among many siblings.

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 parameters are documented; description adds no extra meaning beyond the schema.

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 clearly states converting text to Train-Case, but doesn't differentiate from similar siblings like case_to_kebab or case_to_camel.

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

Usage Guidelines2/5

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

No guidance on when to use this tool over other case converters or text transformations.

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