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code_indent_check

Check code for indentation consistency and validate proper formatting, supporting multiple programming languages.

Instructions

Check and validate code indentation consistency.

Parameters:
    code — Source code to check indentation of.
    language — Programming language (default: 'python').

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": {
      "code": "<code>"
    },
    "name": "code_indent_check"
  }
}

Input Schema

Name	Required	Description	Default
`code`	Yes
`language`	No		python

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv2.4.1Jun 22, 2026

TDQS

A3.5/5.0

Behavior2/5

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

Without annotations, the description should disclose behavior beyond the basic purpose. It only says 'check and validate' without explaining what the validation entails, what the output looks like, or how it handles edge cases. The output schema exists but is not referenced in the description.

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 extremely concise: one introductory sentence and a brief parameter list. Every sentence earns its place; no redundancy or unnecessary detail. The structure is front-loaded with the purpose.

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?

Given the existence of an output schema (not shown), the description does not need to detail return values. However, it lacks behavioral context (e.g., what kind of validation, supported languages for the default) and does not mention any prerequisites or limitations. It is adequate but incomplete for a tool with many siblings.

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?

Schema coverage is 0%, so the description's parameter list adds meaning: it explains that 'code' is source code to check and 'language' is the programming language with default 'python'. This clarifies the role of each parameter beyond the schema's type-only definitions.

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 states 'Check and validate code indentation consistency' with a specific verb and resource. It distinguishes itself from sibling tools like 'indent_text' (which indents) and 'code_beautify' (which formats broadly).

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?

The description provides no guidance on when to use this tool versus alternatives like 'code_beautify' or 'code_stats'. It does not mention when not to use it or any prerequisites.

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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