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ellmos-clatcher-mcp

🇩🇪 Deutsche Version | 🛡️ Security Policy | 📜 Licenses | 📝 Changelog | 📋 llms.txt

npm version License: MIT Node.js Platform Clatcher tests Vitest Security Policy Zero-Egress Third-Party Licenses Marketing Log Last Checked MCP Registry Ready Glama LLM-Ready Ecosystem Umbrella

Claude Patcher -- an MCP server that extends AI coding agents with utility tools they don't have natively. File repair, format conversion, duplicate detection, batch operations, and more.

Use Clatcher when your agent needs reliable local maintenance tools for text files, data files, and project folders: repair invalid JSON, normalize encodings, convert formats, compare folders, rename files safely, and verify checksums without leaving the MCP workflow.

NOTE

AI / LLM Integration Note: All destructive operations (e.g. batch_rename, cleanup_file, fix_json, fix_encoding, fix_umlauts) default to dry-run mode (dry_run: true). Autonomous agents must explicitly specify dry_run: false to execute mutations on disk.

Highlights & Value Proposition

  • 12 Specialized Agent Tools: Extends Claude Code, Cursor, and MCP agents with utilities they lack out-of-the-box (JSON repair, encoding normalization, format conversion, diffing, deduplication, regex batch renaming).

  • Default Dry-Run Guard: Mutating tools run in preview mode (dry_run: true) by default. Agents must pass dry_run: false to write to disk.

  • 100% Local-First & Zero-Egress: Pure local execution over stdio JSON-RPC. No network calls, no cloud telemetry, zero remote attack surface.

  • Atomic File Operations: All disk modifications write to temporary staging buffers before replacement, preventing corrupt or truncated files.

  • Lossless Encoding Preservation: Eliminates Windows cp1252 artifacts, BOM headers, and German umlaut Mojibake (ä, ö, ü, ß) while guaranteeing pristine UTF-8 bytes.

  • Universal Multi-OS Parity: Tested continuously across Ubuntu, Windows, and macOS with native path handling and line endings.

Related MCP server: Code Buddy

🧭 Quick Navigation

#

Section

Focus

01

✨ Highlights & Value Proposition

12 essential tools AI agents lack natively: repair, convert, deduplicate, diff, batch

02

🎯 Target Personas & Discoverability

Autonomous agents, full-stack developers, release engineers, and security compliance

03

⚖️ Comparative Matrix & Alternatives

10-dimension evaluation vs standard agent shells, ad-hoc jq/sed, desktop apps, cloud APIs

04

📐 System Architecture & Data Flow

5-tier architecture flowchart TD for stdio transport and repair engines

05

🔄 End-to-End Execution Sequence

14-step dry-run safety sequence diagram from user prompt to verified disk write

06

🛡️ Core Invariants & Safety Guarantees

10 architectural guarantees ensuring default dry-run, zero-egress, and atomic writes

07

🛠️ Tool Surface & Capabilities

Deep-dive into all 12 MCP tools with parameter schemas and default preview modes

08

⚙️ Installation & Client Setup

Seamless setup for Claude Code CLI, Claude Desktop, Cursor, and npm global

09

🧪 Verification & Automated Tests

163 Vitest tests, 100% green parity, Multi-OS CI matrix across Node.js 20, 22, 24

10

📜 Third-Party Licenses & Transparency

100% permissive open source inventory (0 AGPL / copyleft, zero telemetry)

11

🌐 ellmos MCP Family & Sibling Matrix

9 sibling MCP servers spanning 200+ specialized agent tools

12

🧱 Ecosystem & Partner Suites

Integration with open-bricks desktop suites, BACH text OS, and dev-bricks tools

13

🔒 Security Policy & Incident Reporting

Bilingual security policy, private vulnerability disclosure, 48h response SLA

14

📋 Machine-Readable Context (llms.txt)

Standardized LLM index for agent discovery and RAG crawlers

15

📝 Changelog & Evolution

Release evolution, dry-run security enforcement, and discoverability history

16

⚖️ Liability & Legal Notice

Statutory open-source donation notice under §§ 516 ff. BGB and MIT disclaimer

Target Personas & Discoverability

Persona

Core Needs

Pain Points Solved

Target Discovery Terms

Autonomous AI Agents & Swarms

Non-destructive file repair, preview-first dry-runs, deterministic status receipts

Malformed JSON halting agent loops, unhandled encoding Mojibake corrupting project files

mcp json repair tool, local-first mcp file utilities, dry-run safe agent tools

Full-Stack Developers

Fast multi-format config conversions (JSON/YAML/TOML/XML), regex mass renaming

Cumbersome multi-tool CLI syntax, tedious regex loops, Windows CRLF / BOM pollution

json to toml mcp, yaml xml conversion tool, batch regex rename mcp

DevOps & Release Engineers

Automated multi-hash checksums (SHA-256/SHA-512), folder diffs, ZIP inspection

CI runner tool drift, unverified package hashes, bloated external archive utilities

mcp sha256 checksum, folder diff mcp tool, zip archive mcp runner

Security & Compliance Officers

100% local-first air-gapped stdio execution, zero telemetry, audited permissive licenses

Hidden phone-home telemetry, unknown supply-chain licenses, uncontrolled network egress

zero-egress mcp server, local-first claude mcp, permissive license mcp tools

Comparative Matrix & Alternatives

Dimension

ellmos-clatcher-mcp

Standard Agent Shell

Ad-Hoc CLI (jq/sed)

Heavy Desktop Apps

Cloud Converters / APIs

Primary Interface

Native MCP Stdio (JSON-RPC)

Raw Shell / Bash Exec

Standalone Terminal CLI

GUI Application Window

HTTP REST / Web Page

Safety Guardrails

Built-in dry_run: true Default

Blind Overwrite Risk

Unchecked Shell Writes

Manual Confirmation GUI

Remote Server Storage

Data Privacy & Egress

100% Local-First / Zero-Egress

Local Execution

Local Execution

Local Execution

Remote Cloud Upload

JSON Auto-Repair

Heuristic 6-Rule Repair

Re-generate Full File

Complex JQ Scripting

Manual Syntax Editing

Third-Party Web Paste

Encoding Normalization

Lossless Mojibake Fix

Guesswork / iconv

iconv / enca CLI

Manual File Encoding Chg

Inconsistent Web UTF-8

Multi-Format Conversion

JSON/YAML/TOML/XML/CSV/INI

Prompt Re-writing

Separate CLI Packages

Complex File Exports

Rate-Limited Cloud API

Duplicate Detection

SHA-256 Hash Clustering

None (Custom Script)

Custom bash / find

Standalone Tool (Anti-D)

Not Supported

Batch Regex Renaming

Dry-Run Staged Renamer

Sequential 'mv' loop

rename / sed Scripts

Bulk Rename GUI Utility

Not Supported

Multi-OS Parity

Windows, Linux, macOS

Shell Syntax Quirks

Linux-centric Toolsets

OS-Specific Binaries

Browser-Dependent

License & Audited Security

100% Permissive MIT / BSD

Variable / Unaudited

GPL / Mixed Toolchains

Mixed / Proprietary

Closed Commercial SaaS

System Architecture & Data Flow

graph TD
    Agent["AI Agent / Claude Code / Cursor / IDE"] -->|"MCP JSON-RPC Protocol over Stdio"| Transport["MCP Stdio Transport Layer"]
    Transport --> Server["Clatcher MCP Server Runtime"]
    Server --> Dispatcher{"Tool Dispatcher"}

    Dispatcher -->|"fix_json / cleanup_file"| JsonEngine["JSON Linter & Auto-Fix Engine"]
    Dispatcher -->|"fix_encoding / fix_umlauts"| EncodingEngine["Encoding Normalizer & Mojibake Resolver"]
    Dispatcher -->|"convert_format"| FormatEngine["Format Converter: JSON/YAML/TOML/XML/CSV/INI"]
    Dispatcher -->|"detect_dupes / checksum"| HashEngine["SHA-256 / Multi-Hash Content Engine"]
    Dispatcher -->|"folder_diff / batch_rename"| FileOpsEngine["Folder Diff & Regex Batch Renamer"]
    Dispatcher -->|"archive / zip"| ArchiveEngine["AdmZip Compression Handler"]
    Dispatcher -->|"scan_emoji / regex_test"| RegexEngine["Emoji Scanner & Regex Debugger"]

    JsonEngine --> DryRunGuard{"Dry-Run Guard"}
    EncodingEngine --> DryRunGuard
    FormatEngine --> DryRunGuard
    FileOpsEngine --> DryRunGuard
    ArchiveEngine --> DryRunGuard

    DryRunGuard -->|"dry_run: true (default)"| PreviewReport["Detailed Dry-Run Preview Diff & Status"]
    DryRunGuard -->|"dry_run: false (explicit)"| DiskWrite["Safe Atomic Filesystem Write"]

End-to-End Execution Sequence

sequenceDiagram
    autonumber
    actor User as Developer / Agent Orchestrator
    participant Agent as AI Coding Agent (Claude Code / Cursor)
    participant Stdio as MCP Stdio Protocol (JSON-RPC)
    participant Clatcher as Clatcher MCP Server
    participant Validator as Zod Schema Validator
    participant Engine as Dedicated Tool Engine
    participant Guard as Dry-Run Safety Guard
    participant FS as Local Filesystem

    User->>Agent: Prompt: "Fix broken encoding and trailing commas in config.json"
    Agent->>Stdio: CallTool(name="fix_json", args={path: "config.json", dry_run: true})
    Stdio->>Clatcher: Dispatch JSON-RPC Request
    Clatcher->>Validator: Validate arguments (Zod schema)
    Validator-->>Clatcher: Validated inputs

    Clatcher->>Engine: Run JSON repair pipeline
    Engine->>FS: Read target file content (UTF-8)
    FS-->>Engine: Raw file bytes / string
    Engine->>Engine: Strip comments, trailing commas, single quotes, NULs
    Engine->>Guard: Submit repaired AST / string

    alt dry_run == true (Default Mode)
        Guard->>Guard: Generate diff & mutation preview
        Guard-->>Clatcher: Return diff preview without disk write
    else dry_run == false (Explicit Agent Mutation)
        Guard->>FS: Atomic write to target file via temp buffer
        FS-->>Guard: Write successful
        Guard-->>Clatcher: Return success receipt + bytes written
    end

    Clatcher-->>Stdio: JSON-RPC ToolResult (diff, stats, safety report)
    Stdio-->>Agent: Formatted MCP response
    Agent-->>User: Synthesized result & proposed next steps

Core Invariants & Safety Guarantees

Invariant

Guarantee

Enforcement Mechanism

Default Dry-Run Guard

Mutating tools never alter files silently

All modifying tools (batch_rename, cleanup_file, fix_json, fix_encoding, fix_umlauts, convert_format, archive) default to dry_run: true. Requires explicit dry_run: false to commit changes.

Zero-Egress & Local-First

Zero external telemetry or network calls

100% offline stdio JSON-RPC processing. No telemetry beacons, no external API requests, zero outbound network sockets.

Path Traversal Guard

Confined strictly to authorized file trees

Archive and batch operations validate destination boundaries and resolve relative paths safely against base roots.

Atomic Operations

Resilient against interrupted writes

Modifying pipelines write to staged temporary files before replacing targets, preventing half-written or corrupted outputs.

Non-Elevation User-Mode

Minimal OS privileges required

Runs entirely inside the executing user's standard permissions without requesting sudo/Administrator privileges.

Encoding Preservation

Lossless character encoding round-trip

Fixes Windows cp1252 artifacts, BOM issues, and German umlauts (ä, ö, ü, ß) while preserving pristine UTF-8 byte order.

Multi-Hash Integrity

Bit-level cryptographic verification

Checksum validation supporting SHA-256, SHA-512, MD5, and SHA-1 algorithms.

Multi-OS Parity

Identical behavior across OS platforms

Continuously tested across Linux (ubuntu-latest), Windows (windows-latest), and macOS (macos-latest) with native path separator handling.

Fail-Closed Argument Validation

Invalid parameters rejected before execution

Zod schema validation enforces strict constraints, rejects malformed paths and types, and prevents partial execution.

Deterministic Error Bounds & Receipts

Structured diagnostic reporting on all runs

Invariant tool return contracts: every invocation returns structured JSON-RPC payloads, diff previews, byte counts, and verifiable receipts.

Part of the ellmos MCP family:

Server

Focus

npm

ellmos-filecommander-mcp

Filesystem operations, process management, interactive sessions

ellmos-filecommander-mcp

ellmos-codecommander-mcp

Code analysis, AST parsing, import management

ellmos-codecommander-mcp

ellmos-clatcher-mcp

Utility tools: repair, convert, detect, batch ops

ellmos-clatcher-mcp

n8n-manager-mcp

n8n workflow management via AI assistants

n8n-manager-mcp

ellmos-controlcenter-mcp

MCP stack discovery, profile management, control plane

ellmos-controlcenter-mcp

ellmos-homebase-mcp

LLM memory, knowledge, state, routing, and orchestration

ellmos-homebase-mcp (alpha)

ellmos-servercommander-mcp

Server operations: deploy dry-runs, mail status, log analysis, health checks

ellmos-servercommander-mcp (alpha)

ellmos-blender-use-mcp

Headless Blender asset QA and FBX reimport verification

ellmos-blender-use-mcp (alpha)

open-compute-mcp

Model-agnostic computer use: capture, safety-gated actions, Windows UIA

open-compute-mcp (alpha)

Each server covers a different domain. Use one server, a focused pair, or the full family depending on your workflow.

Discoverability

Primary search terms: ellmos-clatcher-mcp, clatcher mcp, claude patcher, mcp json repair server, mcp encoding fix, model context protocol file repair, claude code utility tools, format conversion mcp tool, duplicate file detection mcp, batch rename mcp, checksum mcp, zip archive mcp.

Tools

Tool

Description

fix_json

Repair broken JSON: strip comments, trailing commas, single quotes, BOM/NUL

fix_encoding

Fix encoding issues: BOM removal, double-encoded UTF-8, cp1252 artifacts

fix_umlauts

Fix broken German umlauts from double-encoding (e.g. ä -> ä)

convert_format

Convert between JSON, YAML, TOML, XML, CSV, and INI

detect_dupes

Find duplicate files by content hash (SHA256), grouped by identical content

folder_diff

Compare two directories, or take a snapshot and diff on next call

batch_rename

Rename files using regex patterns, with dry-run preview

archive

Create, extract, or list ZIP archives

checksum

Calculate file hashes (SHA256, MD5, SHA1, SHA512) with optional verification

cleanup_file

Remove BOM, trailing whitespace, fix line endings, strip NUL bytes

scan_emoji

Find emoji characters in code files

regex_test

Test regex patterns against text, showing all matches with groups

All destructive tools default to dry-run mode and require explicit dry_run: false to write changes.

Installation

Claude Code CLI

claude mcp add ellmos-clatcher-mcp -- npx ellmos-clatcher-mcp

Claude Desktop / Cursor Configuration

Add Clatcher to your claude_desktop_config.json or Cursor MCP settings:

{
  "mcpServers": {
    "clatcher": {
      "command": "npx",
      "args": ["-y", "ellmos-clatcher-mcp"]
    }
  }
}

npm (global)

npm install -g ellmos-clatcher-mcp
claude mcp add ellmos-clatcher-mcp -- ellmos-clatcher

From source

git clone https://github.com/ellmos-ai/ellmos-clatcher-mcp.git
cd ellmos-clatcher-mcp
npm install
npm run build
node dist/index.js

Testing

npm test

163 tests covering all 12 tools, i18n language packs, repository hygiene, and metadata consistency (vitest). The GitHub Actions workflow runs npm ci, TypeScript build, Vitest, and an npm package dry-run on Node.js 20, 22, and 24.

Requirements

  • Node.js >= 20

License

MIT

Third-Party Licenses & Transparency

ellmos-clatcher-mcp adheres strictly to open-bricks and ellmos-ai open-source governance standards. All 7 direct runtime dependencies and 5 development dependencies are 100% permissively licensed (MIT, BSD-3-Clause, BSD-2-Clause, Apache-2.0) with zero copyleft (0% GPL/AGPL) and zero cloud telemetry.

For the comprehensive dependency inventory, SPDX identifiers, and full license texts, see THIRD_PARTY_LICENSES.md.


ellmos-ai Ecosystem

This MCP server is part of the ellmos-ai ecosystem — AI infrastructure, MCP servers, and intelligent tools.

MCP Server Family

Server

Tools

Focus

npm

FileCommander

50

Filesystem, process management, interactive sessions, cloud-lock-safe operations

ellmos-filecommander-mcp

CodeCommander

22

Code analysis, JSON repair, imports, diffs, regex

ellmos-codecommander-mcp

Clatcher

12

File repair, format conversion, batch operations

ellmos-clatcher-mcp

n8n Manager

19

n8n workflow management via AI assistants

n8n-manager-mcp

ControlCenter

34

MCP stack discovery, profile management, control plane

ellmos-controlcenter-mcp

Homebase

51

Local-first LLM memory, knowledge, state, routing, swarm orchestration

ellmos-homebase-mcp (alpha)

ServerCommander

8

Server operations: health checks, log analysis, deploy dry-runs, mail diagnostics

ellmos-servercommander-mcp (alpha)

Blender Use

4

Headless Blender asset QA and FBX reimport verification

ellmos-blender-use-mcp (alpha)

Open Compute

16

Model-agnostic computer use: capture, safety-gated actions, Windows UIA

open-compute-mcp (alpha)

AI Infrastructure

Project

Description

BACH

Local-first text-based OS for LLM agents — 113+ handlers, 550+ tools, SQLite memory

open-compute

Model-agnostic computer-use core powering Open Compute MCP

clutch

Provider-neutral LLM orchestration with auto-routing and budget tracking

rinnsal

Lightweight agent memory, connectors, and automation infrastructure

ellmos-stack

Self-hosted AI research stack (Ollama + n8n + Rinnsal + KnowledgeDigest)

MarbleRun

Autonomous agent chain framework for Claude Code

gardener

Minimalist database-driven LLM OS prototype (4 functions, 1 table)

ellmos-tests

Testing framework for LLM operating systems (7 dimensions)

Desktop Software & Sibling Ecosystem

Our partner organization open-bricks and sister suites bundle AI-native applications and developer tooling:

Repository

Focus

Status

file-bricks/ProFiler

Multi-column PySide6 desktop file manager with smart workspaces

Active

doc-bricks/DokuZen

Document conversion, batch OCR, metadata sanitization

Active

dev-bricks/safe-start-for-codex

Secure workspace preflight and agent bootstrap gates

Active

dev-bricks/DevCenter

Central development cockpit and service manager

Active

dev-bricks/CodeBox

Sandboxed code execution and containerized worker environment

Active

Security Policy

For security vulnerability disclosure channels, supported versions, and our 48-hour response SLA, refer to SECURITY.md.

Machine-Readable Context (llms.txt)

This repository provides a standardized machine-readable context file for AI agents, crawlers, and RAG indexers:

  • llms.txt: Concise manifest of all 12 tools, dry-run safety invariants, sibling MCP tool counts, and CLI invocation examples.

Changelog

For the complete release evolution, version notes, and hygiene audits, see CHANGELOG.md.

Haftung / Liability

Dieses Projekt ist eine unentgeltliche Open-Source-Schenkung im Sinne der §§ 516 ff. BGB. Die Haftung des Urhebers ist gemäß § 521 BGB auf Vorsatz und grobe Fahrlässigkeit beschränkt. Ergänzend gilt der Gewährleistungsausschluss der MIT-Lizenz.

Nutzung auf eigenes Risiko. Keine Wartungszusage, keine Verfügbarkeitsgarantie, keine Gewähr für Fehlerfreiheit oder Eignung für einen bestimmten Zweck.

This project is an unpaid open-source donation under German law. Liability is limited to intent and gross negligence (§ 521 German Civil Code). The MIT License warranty disclaimer applies.

Use at your own risk. No warranty, no maintenance guarantee, no availability guarantee, and no fitness-for-purpose assumed.

Available Tools

12 tools
archiveB

Create, extract, or list ZIP archives.

ParametersJSON Schema
NameRequiredDescriptionDefault
actionYesOperation
dry_runNoPreview create/extract without changing files
overwriteNoAllow extraction to overwrite existing files
extract_toNoExtraction directory (for extract)
archive_pathYesPath to the ZIP file
source_pathsNoFiles/directories to add (for create)

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 carry the burden of behavioral disclosure, but it only restates the action enum from the schema. It does not mention the dry_run default, overwrite safety, the need for source_paths during create, or what 'list' returns. The drug is minimal and adds nothing beyond the schema's existing param descriptions.

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 a single, front-loaded sentence with no redundancy. It is appropriately short, but it sacrifices enough detail that the tool feels under-specified for its complexity.

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 that there is no output schema and no annotations, the description should at least clarify action-dependent parameters, the meaning of dry_run, and the expected behavior of list. It provides none of this, leaving significant ambiguity for an agent trying to invoke the tool correctly.

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 description coverage is 100%, so the schema already documents all parameters and their roles. The description adds no additional parameter-level meaning, so the baseline score 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 names specific verbs (create, extract, list) and a specific resource (ZIP archives), making the tool's purpose immediately clear. It also distinguishes itself from the sibling tools, which are all focused on fixing, converting, or checking files rather than archiving.

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?

There is no explicit guidance about when to use this tool versus an alternative, no conditions, and no exclusions. The intended use is only implied by the operation names and the schema, which is not enough for an agent to confidently decide between this and other file-related utilities.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

batch_renameA

Rename multiple files using regex pattern, prefix/suffix, or counter. Always preview first with dry_run=true.

ParametersJSON Schema
NameRequiredDescriptionDefault
dry_runNotrue = preview only
patternYesRegex pattern to match in filenames
directoryYesDirectory containing files to rename
extensionsNoComma-separated extensions to filter (e.g. 'jpg,png')
replacementYesReplacement string ($1, $2 for capture groups)

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations, the description carries the burden. It mentions renaming and dry_run preview but does not detail actual effects when dry_run=false, permissions needed, or if original files are preserved. Partial transparency.

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 two sentences, front-loading the core purpose and key usage tip. No redundant information.

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 description does not explain return value (e.g., list of renamed files, errors). Without output schema, agents lack understanding of what to expect after execution.

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

Parameters2/5

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

Schema covers 100% of parameters, but the description introduces 'prefix/suffix or counter' as methods not reflected in the schema. This mismatch could mislead an agent into expecting parameters that do not exist.

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 'Rename multiple files' using specific methods (regex, prefix/suffix, counter) and distinguishes this tool from sibling tools like archive or regex_test.

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 advises 'Always preview first with dry_run=true', providing clear usage guidance. It does not explicitly exclude scenarios or name alternatives, but the advice is direct and actionable.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

checksumA

Calculate file hash (SHA256, MD5, SHA1, SHA512). Optionally verify against expected hash.

ParametersJSON Schema
NameRequiredDescriptionDefault
pathYesPath to the file
expectedNoExpected hash to verify against
algorithmNosha256

TDQS

A4.1/5.0
Behavior3/5

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

Description discloses read-only behavior (calculate hash) and optional verification, but lacks details on side effects, permissions, or output format. No annotations exist to supplement.

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?

Single sentence front-loads 'Calculate file hash' and efficiently covers all aspects without waste.

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?

Covers core functionality and algorithms, but lacks output format, error conditions, or behavior when verification fails. No output schema provided.

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?

Adds meaning beyond schema by mentioning optional verification against expected hash. Schema already covers path and algorithm enum, but description ties expected to verify action.

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?

Description clearly states the action (calculate file hash) and resource (file hash), with explicit algorithm options. It distinguishes from sibling tools like archive and batch_rename, which are unrelated.

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?

Description implies usage for hashing files and optional verification. While no explicit when-not or alternatives are provided, sibling tools are distinct, making context clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

cleanup_fileC

Remove BOM, trailing whitespace, fix line endings, remove NUL bytes. Configurable per-option.

ParametersJSON Schema
NameRequiredDescriptionDefault
pathYesPath to the file
dry_runNo
remove_bomNo
remove_nul_bytesNo
ensure_final_newlineNo
normalize_line_endingsNolf
remove_trailing_whitespaceNo

TDQS

C2.6/5.0
Behavior2/5

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

With no annotations, the description should disclose side effects and behavior. It states the tool modifies files but omits whether it edits in-place, creates backups, or its safety profile. The dry_run parameter hints at previewing, but this is not explained.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is short (two sentences) and covers core functionality. While concise, it lacks structured organization and could benefit from bullet points or clearer separation of capabilities.

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 7 parameters and no output schema, the description is incomplete. It fails to mention return values, dry-run behavior, or default configurations. More context is needed for effective use.

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

Parameters2/5

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

Schema description coverage is only 14%. The description says 'Configurable per-option' but does not elaborate on individual parameters. Parameter names are somewhat self-explanatory, but enum and default values are not clarified.

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 lists specific cleanup operations (BOM, whitespace, line endings, NUL bytes) and states configurability. It clearly identifies the tool's function, though it could better differentiate from sibling tools like fix_encoding.

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 on when to use this tool versus alternatives. The description does not mention prerequisites, suitable contexts, or scenarios to avoid.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

convert_formatA

Convert between data formats: JSON, YAML, TOML, XML, CSV, INI. Reads input file and writes output file.

ParametersJSON Schema
NameRequiredDescriptionDefault
dry_runNoPreview conversion without writing the target file
input_pathYesSource file path
output_pathYesTarget file path
input_formatYesSource format
output_formatYesTarget format

TDQS

A3.5/5.0
Behavior2/5

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

With no annotations provided, the description carries full behavioral burden, yet it only states generic I/O ('Reads input file and writes output file'). This is undercut by the schema's dry_run default of true, meaning the tool previews by default and does NOT write unless dry_run is set to false — a behavior the description's 'writes output file' misleadingly implies. No disclosure of overwrite behavior, error handling, or side effects.

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?

Two short sentences with zero filler. The verb and format list are front-loaded, and the second sentence adds the input/output behavior. Every word earns its place.

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?

For a moderate 5-parameter tool with 100% schema coverage, the core function is adequately described. However, with no output schema and no annotations, the absence of any note about the dry_run default (preview-only unless dry_run=false) and lack of clarity about whether write mode overwrites existing files leaves meaningful gaps for an agent deciding how to invoke the tool.

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 description coverage is 100%, so every parameter (input_path, output_path, input_format, output_format, dry_run) is already documented in the schema. The description's format list merely mirrors the enums and adds no new meaning, so the baseline of 3 applies.

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 names a specific verb ('Convert'), a resource (data formats), and enumerates the exact supported formats (JSON, YAML, TOML, XML, CSV, INI). This makes it unmistakably distinct from siblings like fix_json, checksum, and batch_rename, none of which perform cross-format conversion.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The usage context is implied by the phrase 'Convert between data formats' — an agent can infer this is the go-to tool for format translation. However, there is no explicit when-to-use guidance or exclusion; notably, no distinction from fix_json (repair vs. convert) is stated, and no alternative tools are named.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

detect_dupesB

Find duplicate files in a directory by content hash (SHA256). Groups files with identical content.

ParametersJSON Schema
NameRequiredDescriptionDefault
min_sizeNoMinimum file size in bytes (skip empty files)
directoryYesDirectory to scan
recursiveNoScan subdirectories
extensionsNoComma-separated file extensions to check (e.g. 'py,js,ts'). Empty = all files

TDQS

B3.4/5.0
Behavior3/5

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

The description mentions the hashing algorithm and grouping behavior, but lacks details on performance implications (e.g., scanning large directories), file permissions, or handling of symbolic links. Without annotations, more disclosure is needed.

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?

A single sentence that is concise and front-loaded, conveying the essential purpose and method without extraneous words.

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?

The description lacks information about the return format (e.g., list of file groups) and error handling. Given no output schema and no annotations, the description should provide more context about what the tool returns and its behavior.

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% (all 4 parameters described). The description does not add parameter-specific details beyond the schema, but the schema itself is clear. Baseline score 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 states the tool finds duplicate files using SHA256 hashing and groups identical content. This distinguishes it from siblings like 'checksum' (computes hashes) and 'cleanup_file' (deletes files).

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. The list of sibling tools is given but not referenced, and no context is provided about situations where this tool is appropriate or not.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

fix_encodingA

Fix encoding issues: detect and repair BOM, broken UTF-8, cp1252 artifacts. Common on Windows.

ParametersJSON Schema
NameRequiredDescriptionDefault
pathYesPath to the file
dry_runNotrue = analyze only, false = write fixed file

TDQS

A3.5/5.0
Behavior2/5

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

With no annotations, the description must reveal behavioral traits. It mentions 'detect and repair' but does not clarify default behavior (dry_run=true means analysis-only) or potential side effects like file modification. The schema supplies dry-run semantics, but the description adds no behavioral context.

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 at two sentences, delivering the core purpose and common context without wasted words. It is front-loaded with the main action.

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?

While the description explains what encoding issues it fixes and mentions Windows, it lacks information about return values, the analysis/repair process, and default behavior (dry_run). For a tool with no output schema, more detail on output or success indicators would improve completeness.

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 for both parameters (path and dry_run). The tool description does not add extra meaning beyond what the schema already provides, so a baseline score 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 states the tool's purpose: fixing encoding issues, specifically BOM, broken UTF-8, and cp1252 artifacts. It uses a specific verb ('fix') and resource ('encoding issues'), and distinguishes itself from siblings like 'fix_umlauts' by listing concrete problems.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage when encoding problems are present, especially on Windows, but does not explicitly state when to use this tool over alternatives like 'fix_umlauts' or 'convert_format'. No exclusions or prerequisites are provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

fix_jsonA

Repair broken JSON: strip comments, fix trailing commas, convert single quotes, remove BOM/NUL. Supports dry_run mode.

ParametersJSON Schema
NameRequiredDescriptionDefault
pathYesPath to the JSON file
dry_runNotrue = analyze only, false = write repaired file

TDQS

A3.7/5.0
Behavior3/5

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

No annotations provided; the description mentions dry_run mode behavior but does not disclose potential side effects like overwriting the original file or creating backups, which are important for a repair tool.

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, front-loaded sentence that efficiently conveys the tool's action and key features with no redundant information.

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 no output schema, the description omits what the tool returns (e.g., success status, repaired content), leaving an information gap for the agent about the tool's output.

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%, and the description only repeats the dry_run behavior without adding extra meaning about path requirements or expected file format beyond the 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?

The description clearly states the tool's purpose: 'Repair broken JSON' and lists specific fixes (strip comments, fix trailing commas, convert single quotes, remove BOM/NUL), making it distinct from sibling tools like fix_encoding or cleanup_file.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use (for JSON files with syntax issues) but does not provide explicit guidance on when not to use or mention alternatives among siblings like fix_encoding or convert_format.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

fix_umlautsA

Fix broken German umlauts from double-encoding or cp1252 artifacts (ä→ä, ö→ö, etc.)

ParametersJSON Schema
NameRequiredDescriptionDefault
pathYesPath to the file
dry_runNotrue = analyze only

TDQS

A3.6/5.0
Behavior2/5

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

No annotations provided. Description does not disclose whether the tool modifies the file in-place, requires backup, or other behavioral traits. The dry_run parameter hints at analysis, but effects of actual fix are unclear.

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?

Single sentence efficiently conveys purpose and examples with no redundancy.

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 simple tool with two well-described params and no output schema, description covers essential purpose. Could mention return values or confirmation of changes, but adequate for straightforward task.

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 covers 100% of parameters with descriptions. The description adds clarifying examples for the fix operation, but does not add meaning beyond what schema already provides for each parameter.

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?

Description clearly states the tool fixes broken German umlauts from double-encoding or cp1252, with specific character examples (ä→ä, etc.). It distinguishes itself from sibling 'fix_encoding' by being specific to umlauts.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool vs alternatives like 'fix_encoding'. The context of German umlauts is implied, but no when-not-to-use or comparison provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

folder_diffA

Compare two directories, or take a snapshot and compare on next call. Shows new, modified, and deleted files.

ParametersJSON Schema
NameRequiredDescriptionDefault
directoryYesDirectory to compare/snapshot
compare_toNoSecond directory to compare against. Omit for snapshot mode.

TDQS

A4.2/5.0
Behavior3/5

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

No annotations provided, so description must carry burden. It discloses that it shows new/modified/deleted files and has snapshot mode, but does not explicitly state it is read-only or mention performance implications.

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?

Two concise sentences with no unnecessary words. Front-loaded purpose and key details.

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?

Adequately covers purpose and parameters, but missing return format details (no output schema). Fairly complete for a simple diff tool.

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%, and description adds clarification on snapshot mode (omit compare_to). Provides context beyond schema, though somewhat redundant.

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?

Description clearly states the tool compares directories with two modes (direct comparison and snapshot), and lists what it shows (new, modified, deleted files). Differentiates from sibling tools like archive or batch_rename.

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?

Describes two usage modes: provide two directories for comparison or omit compare_to for snapshot. Implicitly guides when to use each mode, but lacks explicit alternatives or when-not-to-use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

regex_testA

Test a regex pattern against text. Shows all matches with groups and positions.

ParametersJSON Schema
NameRequiredDescriptionDefault
textYesText to test against
flagsNoRegex flags (g, i, m, s, u)g
patternYesRegex pattern

TDQS

A3.8/5.0
Behavior3/5

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

No annotations are provided. The description reveals that matches, groups, and positions are shown, but lacks details on edge cases, errors, or performance. Basic behavioral info is present but not comprehensive.

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?

One efficient sentence that conveys core functionality without wasted words.

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?

No output schema; description states outputs (matches with groups and positions) but not format or structure. Adequate for a simple tool, but could be more explicit about return value organization.

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 covers all parameters (100%). The description does not add new meaning to parameters beyond what's in schema; it only describes output behavior. Baseline score 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 states the tool tests a regex pattern against text and lists outputs (matches, groups, positions). It distinguishes from siblings like archive or batch_rename which have unrelated purposes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description does not provide guidance on when to use this tool vs. alternatives, nor does it mention when not to use it. Usage is implied but not explicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

scan_emojiB

Scan code files for emoji characters. Useful for finding accidental emojis in source code.

ParametersJSON Schema
NameRequiredDescriptionDefault
directoryYesDirectory to scan
recursiveNo
extensionsNoComma-separated file extensionspy,js,ts,json,md,txt,yaml,yml,toml

TDQS

B3.3/5.0
Behavior2/5

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

No annotations are provided, so the description must disclose behavioral traits. It does not state that the tool is read-only, nor does it describe any side effects, permissions, or handling of binary files. The scanning nature is implied but not explicitly declared as non-destructive.

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 two sentences, front-loaded with the action, and contains no redundant or unnecessary words. It efficiently communicates the core purpose.

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?

The description lacks information about the tool's output format, which is critical since there is no output schema. It does not explain how the tool returns results (e.g., list of files, count) or handle edge cases. The behavioral transparency gap further reduces completeness.

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

Parameters2/5

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

With 67% schema description coverage, the description adds no extra meaning to the parameters beyond what the schema provides. It does not explain the 'recursive' parameter (which lacks a schema description) or give examples of valid inputs.

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 explicitly states 'Scan code files for emoji characters', which is a specific verb and resource. It clearly differentiates from sibling tools, none of which target emoji scanning. The added phrase 'Useful for finding accidental emojis in source code' reinforces the purpose.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use ('Useful for finding accidental emojis'), but does not provide explicit guidance on when not to use or alternatives. Sibling tools do not overlap, so no exclusion needed, but the guidance is minimal.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 2 tool updatesv1.0.16
    • Changedarchive2 fields changed
      • addedInput schema / properties / dry_run
        Added value: +{
        +  "default": true,
        +  "description": "Preview create/extract without changing files",
        +  "type": "boolean"
        +}
      • addedInput schema / properties / overwrite
        Added value: +{
        +  "default": false,
        +  "description": "Allow extraction to overwrite existing files",
        +  "type": "boolean"
        +}
    • Changedconvert_format1 field changed
      • addedInput schema / properties / dry_run
        Added value: +{
        +  "default": true,
        +  "description": "Preview conversion without writing the target file",
        +  "type": "boolean"
        +}
  2. 12 tool updatesv1.0.5
    • First observedarchive
    • First observedbatch_rename
    • First observedchecksum
    • First observedcleanup_file
    • First observedconvert_format
    • First observeddetect_dupes
    • First observedfix_encoding
    • First observedfix_json
    • First observedfix_umlauts
    • First observedfolder_diff
    • First observedregex_test
    • First observedscan_emoji

TDQS

B3.4/5.0

Scored across 12 tools

Disambiguation4/5

Most tools have clearly distinct purposes (e.g., checksum, archive, folder_diff, batch_rename). However, fix_umlauts, fix_encoding, and cleanup_file overlap in handling encoding issues and BOM removal, which could cause some confusion when choosing between them.

Naming Consistency3/5

The majority of tools follow a verb_noun pattern (fix_*, convert_format, detect_dupes, cleanup_file, scan_emoji), but there are notable exceptions like checksum, archive, folder_diff, and regex_test. The mixed ordering of verb_noun versus noun_verb (or noun-only) breaks a predictable convention.

Tool Count5/5

With 12 tools, the set is well-scoped for a file utilities and data cleanup server. It covers a broad range of functionalities without feeling bloated or insufficient for its apparent purpose.

Completeness4/5

The toolset covers encoding fixes, format conversion, hashing, archiving, duplicate detection, diffing, renaming, and regex testing, which is comprehensive for its domain. Minor gaps like a general text replacement or search tool exist, but they are not critical dead ends given the current utilities.

Maintenance

ActivityActive
ResponsivenessNo issues

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