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ellmos-ai

ellmos-filecommander-mcp

Cleanup File

fc_cleanup_file
Idempotent

Cleans up files by removing UTF-8 BOM, trailing whitespace, NUL bytes, and normalizing line endings. Supports recursive directory processing and dry-run preview to avoid data loss.

Instructions

Cleans up one or more files from common problems.

Args:

  • path (string): Path to file or directory

  • recursive (boolean, optional): Recursive for directories

  • extensions (string, optional): Filter file extensions (e.g. ".txt,.json,.py")

  • remove_bom (boolean): Remove UTF-8 BOM

  • remove_trailing_whitespace (boolean): Remove trailing whitespace

  • normalize_line_endings (string, optional): "lf" | "crlf" | null

  • remove_nul_bytes (boolean): Remove NUL bytes

  • dry_run (boolean): Preview only

Cleans: BOM, NUL bytes, trailing whitespace, line endings

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYesPath to file/directory
recursiveNoRecursive
extensionsNoFilter extensions (.txt,.json)
remove_bomNoRemove BOM
remove_trailing_whitespaceNoTrailing whitespace
normalize_line_endingsNoLine endings
remove_nul_bytesNoRemove NUL bytes
dry_runNoPreview only
Behavior5/5

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

Explicitly lists what it removes (BOM, NUL bytes, trailing whitespace, line endings) and mentions dry_run for preview. Annotations already indicate readOnlyHint=false (modifies), destructiveHint=false (safe but destructive?), idempotentHint=true (safe to re-run). Description adds context about what gets modified, which complements annotations.

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?

Clear structure with arg list and summary. At 16 lines it's somewhat lengthy but each line is informative. Could potentially combine some lines, but overall efficient.

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?

Given 8 parameters, 100% schema coverage, and no output schema, the description adequately covers what the tool does and each parameter. It lacks information about return values (e.g., summary of changes), but since no output schema, a basic listing of what it cleans suffices.

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 100%, so baseline is 3. Description adds semantic groupings (lists types of cleanup, explains dry_run), provides option values for normalize_line_endings, and clarifies extensions filter format. However, it doesn't explain default behavior when specific cleanups are enabled/disabled beyond the defaults in schema.

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?

Clearly states it cleans up files from common problems (BOM, NUL bytes, trailing whitespace, line endings). The verb 'cleans up' and resource 'file' are specific, and the description distinguishes it from siblings like fc_fix_encoding or fc_fix_json.

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 lists the problems it cleans, implying when to use it (files with those issues). No explicit when-not to use or alternatives, but among siblings like fc_fix_encoding (presumably fixes encoding specifically), this tool's generic cleanup is clear.

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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