MCP Utility Tools

convert_case

Convert text between naming styles such as snake_case, camelCase, PascalCase, kebab-case, and more. Choose the target case to normalize your strings.

Instructions

Convert text between naming styles: snake_case, camelCase, PascalCase, kebab-case, CONSTANT_CASE, Title Case, or Sentence 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>",
      "target_case": "<target_case>"
    },
    "name": "convert_case"
  }
}

Input Schema

Name	Required	Description	Default
`text`	Yes	The text to convert (any of the above styles, or plain words).
`target_case`	Yes	One of "snake", "camel", "pascal", "kebab", "constant", "title", "sentence".

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Aug 11, 2026

TDQS

A3.8/5.0

Behavior2/5

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

No annotations are present, so the description carries the full burden of behavioral disclosure. It only states the basic conversion operation without addressing edge cases, input validation, output format, or error handling. For a stateless conversion tool this is a notable gap, though not severely misleading.

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 a single, well-structured sentence that front-loads the action and lists the supported cases. No extraneous information is present, earning a perfect score.

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 simple two-parameter converter with a clear schema and an output schema present, the description adequately captures the tool's purpose. It could mention behavior for unexpected input or reversibility, but the core use case is fully covered, making it nearly complete.

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?

The input schema fully describes both parameters with allowed values and examples. The description adds no additional parameter semantics beyond restating the target case options, which are already in the schema. With 100% schema coverage, the baseline of 3 is appropriate.

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 identifies the tool as a text converter between specific naming conventions, enumerating all supported styles with concrete examples (snake_case, camelCase, etc.). This differentiates it from sibling tools like convert_units or hash_text, making its function unambiguous.

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 implies use whenever text needs to be transformed into a different casing style, and the sibling list provides context for alternatives. However, it does not explicitly state exclusions or when not to use this tool, such as for non-case text transformations, so it falls short of a 5.

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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