QIDI Studio MCP Server

QIDI Studio MCP Server

Manages QIDI Studio 3D printing profiles locally, enabling AI assistants to read and write filament, process, and machine presets via the Model Context Protocol.

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QIDI Studio MCP Server

ALPHA macOS Tested Windows Linux

MCP server for managing QIDI Studio 3D printing profiles locally.

Русская версия → README_RU.md

Connects AI assistants (Claude, Cursor, etc.) directly to your local QIDI Studio configuration files. Reads and writes filament, process, and machine presets via the Model Context Protocol.

⚠️ ALPHA VERSION — USE AT YOUR OWN RISK The author is not responsible for lost or corrupted presets. Always backup before using MCP.


Why this exists

Real-time printer control — start, pause, heat — is easier done by hand in QIDI Studio. MCP is not needed there.

Print settings, however, are a completely different story.

QIDI Studio contains hundreds of parameters: temperatures, retractions, fan speeds, support patterns, gaps, accelerations. Every material (PETG, PLA, ABS, composites) and every nozzle requires its own balance. Memorizing all of this and not missing a detail is difficult even for an experienced user.

This MCP solves exactly that: it helps AI help you pick, create, and save the right preset — safely, controllably, and with minimal token consumption. You still see the final settings in QIDI Studio before sending the job to print.

The core problem

  • Too many settings across different materials and nozzles
  • Easy to forget a key parameter (e.g., fan_max_speed for PETG or support_type for complex geometry)
  • Manually browsing JSON profiles in folders is inconvenient and slow
  • No centralized way to "try option A, compare with option B, save the best"

What the MCP does

  • Auto-detects the current project from QIDI Studio configs — no need to type printer and nozzle manually
  • Filters profiles by printer + nozzle + material — you see only what is relevant
  • Safely creates presets — AI proposes, you confirm, a user preset is saved without overwriting system ones
  • Validates — checks temperatures, enum values, and unknown keys before writing to disk

⚠️ Backup First

The server writes directly to QIDI Studio's configuration files. Mistakes happen.

Where your presets are stored:

OS Path
macOS ~/Library/Application Support/QIDIStudio/user/default/
Windows %APPDATA%\QIDIStudio\user\default\
Linux ~/.config/QIDIStudio/user/default/

Backup:

# macOS
cp -r ~/Library/Application\ Support/QIDIStudio/user/default ~/Desktop/QIDIStudio-backup

# Windows
xcopy "%APPDATA%\QIDIStudio\user\default" "%USERPROFILE%\Desktop\QIDIStudio-backup" /E /I

# Linux
cp -r ~/.config/QIDIStudio/user/default ~/QIDIStudio-backup

Restore:

# macOS
cp -r ~/Desktop/QIDIStudio-backup/* ~/Library/Application\ Support/QIDIStudio/user/default/

# Windows
xcopy "%USERPROFILE%\Desktop\QIDIStudio-backup" "%APPDATA%\QIDIStudio\user\default" /E /Y

# Linux
cp -r ~/QIDIStudio-backup/* ~/.config/QIDIStudio/user/default/

Quick Start

Requirements

  • Python 3.10+
  • QIDI Studio installed (the server reads its built-in profiles)

Install

git clone https://github.com/QIDITECH/QIDIStudio-MCP.git
cd QIDIStudio-MCP
pip install -r requirements.txt

Connect to Claude Desktop

Option 1: Automatic (uv)

uv run mcp install mcp_server.py --name "QIDI Studio"

Option 2: Manual

Edit Claude Desktop settings:

  • macOS: ~/Library/Application Support/Claude/settings.json
  • Windows: %APPDATA%\Claude\settings.json
{
  "mcpServers": {
    "qidi-studio": {
      "command": "python",
      "args": ["/path/to/QIDIStudio-MCP/mcp_server.py"]
    }
  }
}

Environment Variables

Variable Purpose Example
QIDI_STUDIO_CONFIG_PATH Override user presets path ~/Library/Application Support/QIDIStudio
QIDI_STUDIO_SYSTEM_PATH Override system profiles path /Applications/QIDIStudio.app/Contents/Resources/profiles

AI Agent Instructions (Required)

Step 1: Detect current project (auto)

get_current_project_info()  → {"machine": "Q2 0.4 nozzle", "filament": "Generic PETG...", "process": "0.20mm Standard @Q2"}

If a project is detected, propose using the detected printer instead of asking. Still confirm with the user.

Step 2: If no project detected, ask the user

Show numbered lists and ask to pick by number. Use compact=True and limit=10 to keep responses tiny:

Detected printers:
1. Q2
2. X-Plus 4
3. X-Max 4
Which printer? (enter number):

Nozzles for Q2:
1. 0.2
2. 0.4
3. 0.6
4. 0.8
Which nozzle? (enter number):

Filter semantics for process profiles: 0.20mm Standard @Q2 has no nozzle in its name. It matches printer_model="Q2" + nozzle_size="0.4" only if its compatible_printers contains "Q2 0.4 nozzle". Without compatible_printers or for a non-existent nozzle, the profile is filtered out. Always pass both printer and nozzle for process queries.

list_profiles response format: On success, an array of profiles is returned. On empty filtered result, an object {"profiles": [], "debug": {...}, "hint": "..."} is returned. Check the response type: array = results, object with profiles = empty + reasons.

Debug on empty results: The debug field contains total_available, sample_names, name_matches, fallback_matches, and excluded_examples — use this to understand why the filter dropped profiles.

Filter semantics for material_type: Only applies to filament profiles. For process, use printer_model instead.

Step 3: Always confirm before creating/updating

I will create preset "Glass PETG @Qidi Q2 0.4" with:
- Printer: Q2 (0.4 nozzle)
- Base: Generic PETG @Qidi Q2 0.4 nozzle
- Settings: nozzle_temperature=["255"], fan_max_speed=["0"]
Confirm? (yes/no)

NEVER do this

❌ list_profiles("filament", "all")          → returns 1000+ profiles
❌ list_profiles("filament", "all", material="PLA") → still hundreds across all printers

Correct token-efficient flow

get_current_project_info()                → auto-detect or skip
get_available_printers()                  → if not detected
get_available_nozzles("Q2")               → if not detected
get_available_vendors()                   → ["Generic", "QIDI", "HATCHBOX"]
get_available_materials("Q2", "0.4")     → ["PETG", "PLA"]
list_profiles("filament", "all", "Q2", "0.4", "PETG", 50, 0, True)  → 3 compact profiles
read_profile(...)                         → inspect one
// CONFIRM with user before:
create_preset(...)                        → create custom

MCP Tools

Discovery (call these first, always filtered)

Tool Purpose
get_current_project_info() Auto-detect active printer/filament/process from QIDI Studio config
get_available_printers() Printer models detected from local profiles
get_available_nozzles(printer) Nozzle sizes for a specific printer
get_available_vendors() Filament brands: Generic, QIDI, HATCHBOX, etc.
get_available_materials(printer, nozzle) Materials for a printer+nozzle combo
get_available_process_qualities(printer, nozzle) Quality presets for a printer+nozzle

Profile Operations

Tool Purpose
discover_settings(type, prefix, keys_only) Settings with prefix filter and keys-only mode to save tokens
list_profiles(type, source, printer, nozzle, material, limit, offset, compact) FILTERED list — never omit filters
read_profile(type, name, source) Read a profile's JSON
create_preset(type, name, base, settings) Create user preset inheriting from base (only overrides passed keys)
update_preset(type, name, settings) Update existing user preset (only passed keys)
clone_preset(type, source, new_name) Full copy with resolved inheritance
delete_preset(type, name) Remove user preset
compare_profiles(type, a, b, max_diffs) Show differences (limit output)
validate_preset_settings(type, settings, strict) Safety check + enum validation
get_version_info() Detected QIDI Studio version
get_config_directory() User config path

Token-Saving Design

This MCP is designed so that the AI agent spends minimum tokens on routine and maximum on decision-making.

  • Settings are read from local JSON profiles directly — no hardcoded or outdated lists. discover_settings returns only what actually exists in the QIDI Studio profiles on your machine.
  • Filtering cuts 90%+ noiselist_profiles with printer_model, nozzle_size, and material_type returns 3–15 profiles instead of 1000+.
  • Compact modecompact=True returns only name and source, no paths or metadata.
  • Prefix filtering for settingsdiscover_settings("process", "support_", ...) returns dozens of fields instead of hundreds.
  • Keys-only modekeys_only=True gives a list of parameter names without values when you just need to know what is available.
  • Pagination and limitslimit=50 and offset prevent accidental dumps of the entire catalog.
  • Debug on empty results — if a filter returns [], the response includes debug with excluded_examples explaining why profiles were dropped. No guessing needed.

Examples

Create Glass PETG preset

get_available_printers()
get_available_nozzles("Q2")
get_available_vendors()
get_available_materials("Q2", "0.4")
list_profiles("filament", "all", "Q2", "0.4", "PETG", 50, 0, True)
create_preset("filament", "Glass PETG @Qidi Q2 0.4", "Generic PETG @Qidi Q2 0.4 nozzle",
  '{"nozzle_temperature": ["255"], "fan_max_speed": ["0"]}')

Vase mode process

get_available_printers()
get_available_nozzles("Q2")
get_available_process_qualities("Q2", "0.4")
list_profiles("process", "all", "Q2", "0.4", "", 10, 0, True)
clone_preset("process", "0.12mm High Quality @Q2", "0.12mm Vase @Q2", "system")
update_preset("process", "0.12mm Vase @Q2", '{"spiral_mode": "1", "wall_loops": "1"}')

Find support-related settings

discover_settings("process", "support_", False)
discover_settings("process", "", True)  # just key names

Testing

python test_server.py

Project Structure

QIDIStudio-MCP/
├── mcp_server.py           # MCP server (tools)
├── qidi_config_manager.py  # Local profile manager
├── test_server.py          # Test suite
├── requirements.txt        # Dependencies (mcp)
├── .gitignore
├── README.md               # This file (EN)
└── README_RU.md            # Russian version

Tested Platforms

Platform Status
macOS ✅ Tested (primary)
Windows ⚠️ Not tested
Linux ⚠️ Not tested

Please open an issue if you test on Windows or Linux.


License

MIT

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