ellmos-clatcher-mcp
OfficialThis server is a local-first MCP toolset that extends AI agents with file maintenance, repair, conversion, and analysis utilities.
fix_json: Repairs broken JSON (comments, trailing commas, single quotes, BOM/NUL) with dry-run preview.fix_encoding: Fixes BOM, broken UTF-8, and cp1252 encoding artifacts.fix_umlauts: Repairs mangled German umlauts (e.g.ä→ä).convert_format: Converts between JSON, YAML, TOML, XML, CSV, and INI.detect_dupes: Finds duplicate files by SHA-256 content hash, with filtering and recursion.folder_diff: Compares two directories or snapshots a directory for later diffing.batch_rename: Renames files via regex/capture groups with dry-run preview.archive: Creates, extracts, or lists ZIP archives safely.checksum: Computes or verifies SHA-256, MD5, SHA-1, and SHA-512 hashes.cleanup_file: Removes BOM, NUL bytes, trailing whitespace, and normalizes line endings.scan_emoji: Scans code files for emoji characters.regex_test: Tests regex patterns and reports matches with groups and positions.Mutating tools default to
dry_run: true, so changes require explicitdry_run: false.Operates 100% locally over stdio with zero network egress.
ellmos-clatcher-mcp
🇩🇪 Deutsche Version | 🛡️ Security Policy | 📜 Licenses | 📝 Changelog | 📋 llms.txt
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.
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 passdry_run: falseto 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 | 12 essential tools AI agents lack natively: repair, convert, deduplicate, diff, batch | |
02 | Autonomous agents, full-stack developers, release engineers, and security compliance | |
03 | 10-dimension evaluation vs standard agent shells, ad-hoc jq/sed, desktop apps, cloud APIs | |
04 | 5-tier architecture flowchart TD for stdio transport and repair engines | |
05 | 14-step dry-run safety sequence diagram from user prompt to verified disk write | |
06 | 10 architectural guarantees ensuring default dry-run, zero-egress, and atomic writes | |
07 | Deep-dive into all 12 MCP tools with parameter schemas and default preview modes | |
08 | Seamless setup for Claude Code CLI, Claude Desktop, Cursor, and npm global | |
09 | 163 Vitest tests, 100% green parity, Multi-OS CI matrix across Node.js 20, 22, 24 | |
10 | 100% permissive open source inventory (0 AGPL / copyleft, zero telemetry) | |
11 | 9 sibling MCP servers spanning 200+ specialized agent tools | |
12 | Integration with open-bricks desktop suites, BACH text OS, and dev-bricks tools | |
13 | Bilingual security policy, private vulnerability disclosure, 48h response SLA | |
14 | Standardized LLM index for agent discovery and RAG crawlers | |
15 | Release evolution, dry-run security enforcement, and discoverability history | |
16 | 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 |
|
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 |
|
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 |
|
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 |
|
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 | 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 stepsCore Invariants & Safety Guarantees
Invariant | Guarantee | Enforcement Mechanism |
Default Dry-Run Guard | Mutating tools never alter files silently | All modifying tools ( |
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 ( |
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 ( |
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 |
Filesystem operations, process management, interactive sessions | ||
Code analysis, AST parsing, import management | ||
Utility tools: repair, convert, detect, batch ops | ||
n8n workflow management via AI assistants | ||
MCP stack discovery, profile management, control plane | ||
LLM memory, knowledge, state, routing, and orchestration |
| |
Server operations: deploy dry-runs, mail status, log analysis, health checks |
| |
Headless Blender asset QA and FBX reimport verification |
| |
Model-agnostic computer use: capture, safety-gated actions, Windows UIA |
|
Each server covers a different domain. Use one server, a focused pair, or the full family depending on your workflow.
Discoverability
npm:
ellmos-clatcher-mcpGitHub:
ellmos-ai/ellmos-clatcher-mcpMCP Registry metadata:
server.jsondeclares the officialio.github.ellmos-ai/ellmos-clatcher-mcppackage identity.Glama.ai Registry:
glama.jsonmanifest for Glama MCP ecosystem.LLM index:
llms.txtsummarizes the tool surface for agents and registry crawlers.
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 |
| Repair broken JSON: strip comments, trailing commas, single quotes, BOM/NUL |
| Fix encoding issues: BOM removal, double-encoded UTF-8, cp1252 artifacts |
| Fix broken German umlauts from double-encoding (e.g. |
| Convert between JSON, YAML, TOML, XML, CSV, and INI |
| Find duplicate files by content hash (SHA256), grouped by identical content |
| Compare two directories, or take a snapshot and diff on next call |
| Rename files using regex patterns, with dry-run preview |
| Create, extract, or list ZIP archives |
| Calculate file hashes (SHA256, MD5, SHA1, SHA512) with optional verification |
| Remove BOM, trailing whitespace, fix line endings, strip NUL bytes |
| Find emoji characters in code files |
| 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-mcpClaude 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-clatcherFrom source
git clone https://github.com/ellmos-ai/ellmos-clatcher-mcp.git
cd ellmos-clatcher-mcp
npm install
npm run build
node dist/index.jsTesting
npm test163 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
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 |
50 | Filesystem, process management, interactive sessions, cloud-lock-safe operations | ||
22 | Code analysis, JSON repair, imports, diffs, regex | ||
12 | File repair, format conversion, batch operations | ||
19 | n8n workflow management via AI assistants | ||
34 | MCP stack discovery, profile management, control plane | ||
51 | Local-first LLM memory, knowledge, state, routing, swarm orchestration |
| |
8 | Server operations: health checks, log analysis, deploy dry-runs, mail diagnostics |
| |
4 | Headless Blender asset QA and FBX reimport verification |
| |
16 | Model-agnostic computer use: capture, safety-gated actions, Windows UIA |
|
AI Infrastructure
Project | Description |
Local-first text-based OS for LLM agents — 113+ handlers, 550+ tools, SQLite memory | |
Model-agnostic computer-use core powering Open Compute MCP | |
Provider-neutral LLM orchestration with auto-routing and budget tracking | |
Lightweight agent memory, connectors, and automation infrastructure | |
Self-hosted AI research stack (Ollama + n8n + Rinnsal + KnowledgeDigest) | |
Autonomous agent chain framework for Claude Code | |
Minimalist database-driven LLM OS prototype (4 functions, 1 table) | |
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 |
Multi-column PySide6 desktop file manager with smart workspaces | Active | |
Document conversion, batch OCR, metadata sanitization | Active | |
Secure workspace preflight and agent bootstrap gates | Active | |
Central development cockpit and service manager | Active | |
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 toolsarchiveB
Create, extract, or list ZIP archives.
| Name | Required | Description | Default |
|---|---|---|---|
| action | Yes | Operation | |
| dry_run | No | Preview create/extract without changing files | |
| overwrite | No | Allow extraction to overwrite existing files | |
| extract_to | No | Extraction directory (for extract) | |
| archive_path | Yes | Path to the ZIP file | |
| source_paths | No | Files/directories to add (for create) |
TDQS
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.
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.
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.
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.
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.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| dry_run | No | true = preview only | |
| pattern | Yes | Regex pattern to match in filenames | |
| directory | Yes | Directory containing files to rename | |
| extensions | No | Comma-separated extensions to filter (e.g. 'jpg,png') | |
| replacement | Yes | Replacement string ($1, $2 for capture groups) |
TDQS
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.
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.
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.
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.
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.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes | Path to the file | |
| expected | No | Expected hash to verify against | |
| algorithm | No | sha256 |
TDQS
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.
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.
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.
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.
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.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes | Path to the file | |
| dry_run | No | ||
| remove_bom | No | ||
| remove_nul_bytes | No | ||
| ensure_final_newline | No | ||
| normalize_line_endings | No | lf | |
| remove_trailing_whitespace | No |
TDQS
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.
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.
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.
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.
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.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| dry_run | No | Preview conversion without writing the target file | |
| input_path | Yes | Source file path | |
| output_path | Yes | Target file path | |
| input_format | Yes | Source format | |
| output_format | Yes | Target format |
TDQS
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.
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.
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.
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.
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.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| min_size | No | Minimum file size in bytes (skip empty files) | |
| directory | Yes | Directory to scan | |
| recursive | No | Scan subdirectories | |
| extensions | No | Comma-separated file extensions to check (e.g. 'py,js,ts'). Empty = all files |
TDQS
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.
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.
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.
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.
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.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes | Path to the file | |
| dry_run | No | true = analyze only, false = write fixed file |
TDQS
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.
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.
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.
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.
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.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes | Path to the JSON file | |
| dry_run | No | true = analyze only, false = write repaired file |
TDQS
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.
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.
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.
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.
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.
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.)
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes | Path to the file | |
| dry_run | No | true = analyze only |
TDQS
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.
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.
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.
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.
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.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| directory | Yes | Directory to compare/snapshot | |
| compare_to | No | Second directory to compare against. Omit for snapshot mode. |
TDQS
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.
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.
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.
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.
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.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Text to test against | |
| flags | No | Regex flags (g, i, m, s, u) | g |
| pattern | Yes | Regex pattern |
TDQS
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.
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.
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.
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.
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.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| directory | Yes | Directory to scan | |
| recursive | No | ||
| extensions | No | Comma-separated file extensions | py,js,ts,json,md,txt,yaml,yml,toml |
TDQS
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.
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.
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.
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.
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.
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.
2 tool updates
v1.0.16- Changed
archive2 fields changed- added
Input schema / properties / dry_runAdded value: +{ + "default": true, + "description": "Preview create/extract without changing files", + "type": "boolean" +} - added
Input schema / properties / overwriteAdded value: +{ + "default": false, + "description": "Allow extraction to overwrite existing files", + "type": "boolean" +}
- Changed
convert_format1 field changed- added
Input schema / properties / dry_runAdded value: +{ + "default": true, + "description": "Preview conversion without writing the target file", + "type": "boolean" +}
12 tool updates
v1.0.5- First observed
archive - First observed
batch_rename - First observed
checksum - First observed
cleanup_file - First observed
convert_format - First observed
detect_dupes - First observed
fix_encoding - First observed
fix_json - First observed
fix_umlauts - First observed
folder_diff - First observed
regex_test - First observed
scan_emoji
TDQS
Scored across 12 tools
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.
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.
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.
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
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