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tool-mcp-server

tool-mcp-server

by maple-awa
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
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tool_case_converter

Convert text into upper, lower, title, camelCase, snake_case, or kebab-case. Provides multiple case conversions for any input string.

Instructions

[text] Case Converter

Parameters:

  • text (string): Text

Outputs:

  • upper (string): UPPER

  • lower (string): lower

  • title (string): Title

  • camel (string): camelCase

  • snake (string): snake_case

  • kebab (string): kebab-case

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "tool_case_converter"
  }
}

Input Schema

NameRequiredDescriptionDefault
textNoText
camelNocamelCase
kebabNokebab-case
lowerNolower
snakeNosnake_case
titleNoTitle
upperNoUPPER

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.1/5.0
Behavior2/5

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

With no annotations, the description must disclose behavior, but it only lists outputs without explaining whether all are returned together, edge-case handling, or the mismatch with the input schema. The schema lists output names as input properties, and the description does not clarify this discrepancy, leaving the agent uncertain about the actual API contract.

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?

The description is concise and well-structured, with a heading, parameter list, and output list. It avoids fluff and is easy to scan, though the '[text]' placeholder is slightly cryptic and could be clearer.

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?

The tool is simple, and the description provides core input/output info. However, it leaves the schema ambiguity unresolved and does not specify return behavior or prerequisites. Given no output schema or annotations, the description is adequate but not fully complete.

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

Parameters4/5

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

The description adds essential meaning by marking 'text' as the sole input and the rest as outputs, which the input schema fails to distinguish. This clarifies the intended use despite the schema's ambiguous listing. However, it does not elaborate on text constraints or formatting details beyond the names.

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 'Case Converter' and lists the input parameter (text) and the output variants (upper, lower, title, camel, snake, kebab), clearly indicating it converts text to multiple case formats. It distinguishes itself from sibling converter tools, though it could be more explicit with a sentence like 'Converts text to different case formats.'

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 is provided on when to use this tool versus alternatives. There is no mention of typical use cases, limitations, or exclusions. The description simply presents the tool without contextualizing its usage.

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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