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lukisch

ellmos FileCommander MCP Server

Cleanup File

fc_cleanup_file
Idempotent

Clean up files by removing BOM, NUL bytes, trailing whitespace, and normalizing line endings. Supports dry-run to preview changes.

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
dry_runNoPreview only
recursiveNoRecursive
extensionsNoFilter extensions (.txt,.json)
remove_bomNoRemove BOM
remove_nul_bytesNoRemove NUL bytes
normalize_line_endingsNoLine endings
remove_trailing_whitespaceNoTrailing whitespace
Behavior3/5

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

Annotations indicate idempotentHint=true and destructiveHint=false, so the tool is safe non-destructive and idempotent. The description adds that it modifies files by cleaning problems, but doesn't elaborate on behavior like what happens on dry run or whether modifications are in-place. Given the annotations cover safety, this is adequate but not exceptional.

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 well-structured with a clear first sentence, a parameter list, and a summary. It is not overly verbose, but could be slightly more concise by omitting redundant parameter details already present in the schema.

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?

Given 8 parameters and no output schema, the description fails to explain the return value or behavior after execution (e.g., what is returned, whether files are modified in-place, error handling). This leaves usability gaps for an AI agent.

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% with clear descriptions. The description lists all parameters with types and defaults, and adds a summary of what is cleaned. However, it mostly reiterates schema info without adding deeper semantics or examples. Baseline 3 is appropriate.

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 the tool's purpose: cleaning up common file problems like BOM, NUL bytes, trailing whitespace, and line endings. It uses a specific verb ('cleans up') and identifies the resource ('files'). However, it doesn't explicitly differentiate from sibling tools like fc_fix_encoding or fc_validate_json, which may have overlapping functionality.

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 given on when to use this tool versus alternatives, or when not to use it. The description is purely functional and lacks context about prerequisites, edge cases, or preferred use cases.

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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