ice-puzzle-mcp

ice-puzzle-mcp

An MCP server for building, validating, and publishing Ice Puzzle levels with AI assistants, featuring integrated solver feedback and quality checks. It provides over 60 tools for level design, difficulty analysis, and benchmarking AI performance on design tasks.

Category
Visit Server

README

ice-puzzle-mcp

MCP (Model Context Protocol) server for building, validating, and publishing Ice Puzzle levels with AI assistants.

Why Use This

  • Build puzzle levels quickly with solver-backed feedback after each edit.
  • Keep design fun: sketch an idea, ask the AI to iterate, and immediately test solvability and quality gates.
  • Compare AI (Codex vs Claude Code) models on the same level-design task with objective outputs (solve_level, analyze_difficulty, validate_quality_gate, check_publish_readiness).
  • Publish with confidence using preview + readiness checks instead of one-shot blind publishing.

Quick Start (Codex / Claude)

1) Install

npm install -g ice-puzzle-mcp

Or run without global install:

npx -y ice-puzzle-mcp setup

2) Get an API key (required for remote drafts, preview, publish)

  1. Open https://ice-puzzle-game.web.app/settings.
  2. Open Settings > Developer.
  3. Sign in with Google.
  4. Click generate API key.
  5. Copy the key (ipk_...) and keep it private.

3) Run setup

ice-puzzle-mcp setup

If you did not install globally, use:

npx -y ice-puzzle-mcp setup

Setup will:

  • Validate your API key by exchanging it once.
  • Save config to ~/.ice-puzzle-mcp/config.json with 0600 permissions.
  • Register MCP in Codex and Claude when available.
  • Auto-discover Codex CLI from PATH and standard app locations (including /Applications/Codex.app/Contents/Resources/codex).

4) Verify registration

codex mcp list --json

You should see an ice-puzzle-mcp entry. Restart your AI client after setup.

What It Does

  • Exposes 63 tools for level editing, solve diagnostics, design assistance, layout seeding, quality gates, campaign examples, local snapshots, preview staging, and Firebase publishing.
  • Mirrors gameplay mechanics from the ice_puzzle app solver/runtime.
  • Enforces canonical publish checks: par == shortest, timeout death at moves >= par, warp-stop semantics, and hot coals shortcut diagnostics.
  • Adds design-assist workflow helpers: dry-run placement tests, reachability maps, stop-point suggestions, layout seeding, undo/redo, and revert-to-last-solvable recovery.
  • Supports offline-first design (all local tools work without auth).

Install

npm install ice-puzzle-mcp

Run

npx ice-puzzle-mcp serve

Optional Auth Setup (for remote drafts + publishing)

  1. Open https://ice-puzzle-game.web.app/settings.
  2. Sign in with Google.
  3. Go to Settings > Developer and generate an API key.
  4. Run setup and paste your key when prompted:
npx ice-puzzle-mcp setup

By default, setup uses the correct exchange URL automatically and does not prompt for it. Only use advanced mode if you need to override it:

npx ice-puzzle-mcp setup --advanced

Setup stores config at ~/.ice-puzzle-mcp/config.json with mode 0600, registers ice-puzzle-mcp in Codex via codex mcp add when Codex is installed (including app-bundle fallback discovery at /Applications/Codex.app/Contents/Resources/codex), and updates your Claude MCP config (~/.claude/.mcp.json or ~/.claude/mcp.json) using a unique server key (ice-puzzle-mcp, ice-puzzle-mcp-2, ... if needed).

Firebase client config is no longer hardcoded in the package. By default, it is loaded from https://<project-id>.web.app/__/firebase/init.json (project ID from config/env, default ice-puzzle-game), or you can provide explicit ICE_PUZZLE_FIREBASE_* environment variables / firebase overrides in config.

AI Model Comparison Workflow

Use this project as a repeatable level-design benchmark between models.

  1. Give each model the same prompt and constraints.
  2. Require title provenance in every generated level name:
  • Prefix with [Claude] for Claude-generated levels.
  • Prefix with [Codex] for Codex-generated levels.
  • Example: [Codex] L14 - Warp Plate Gauntlet.
  1. Require the same build loop: edit one mechanic at a time and run solve_level after each change.
  2. Collect objective outputs:
  • solve_level (solvable, shortest path, moves)
  • analyze_difficulty (difficulty signals, direction balance)
  • validate_quality_gate (pass/fail checks)
  • check_publish_readiness (publish blockers)
  1. Compare results on:
  • Correctness (solvable, no shortcuts, par integrity)
  • Design quality (path structure, mechanic integration, anti-shortcut robustness)
  • Efficiency (how many edits/iterations needed to pass all checks)
  1. Optionally publish or preview top candidates and run playthrough feedback.

This makes AI-vs-AI comparisons concrete instead of subjective.

Tool Categories

Level Management (10)

  • create_level
  • get_level
  • save_local_draft
  • load_local_draft
  • list_drafts
  • delete_draft
  • import_level
  • export_level
  • rename_level
  • clear_level

History + Recovery (3)

  • undo
  • redo
  • revert_to_last_solvable

Tile + Region Operations (6)

  • place_tile
  • remove_tile
  • move_tile
  • place_tiles_batch
  • fill_region
  • clear_region

Grid + Positions (5)

  • set_grid_size
  • set_start
  • set_goal
  • set_par
  • set_par_to_shortest

Special Elements (10)

  • add_warp_pair
  • remove_warp
  • add_thin_ice
  • remove_thin_ice
  • add_pushable_rock
  • remove_pushable_rock
  • set_pressure_plate
  • remove_pressure_plate
  • set_barrier
  • remove_barrier

Solver + Validation (10)

  • solve_level
  • test_placement
  • reachable_from
  • suggest_stop_points
  • simulate_move
  • simulate_playthrough
  • analyze_difficulty
  • validate_quality_gate
  • check_publish_readiness
  • visualize_level

Layout Seeding (2)

  • seed_layout_pattern
  • suggest_skeleton_layout

Rules + Docs (4)

  • get_game_rules
  • get_tile_types
  • get_level_requirements
  • interaction_faq

Campaign Examples (2)

  • list_campaign_examples
  • get_campaign_example

Firebase / Publishing (11)

  • save_draft
  • load_draft
  • list_remote_drafts
  • delete_remote_draft
  • list_my_published_levels
  • get_my_published_level
  • unpublish_level
  • restore_published_level
  • publish_level
  • preview_level
  • auth_status

Canonical Rules Enforced

  • Non-tutorial levels may have tied optimal paths, but shortest optimal length must equal par.
  • Any path shorter than par is a hard failure.
  • Timeout death is strict: if moves >= par and goal not reached, run fails (WASTED).
  • Warp behavior: stepping on warp teleports to pair and ends movement.
  • Hot Coals/hot tile behavior: -5 HP on landing and -5 HP/sec while standing.
  • Thin ice behavior: crossing marks tile broken; entering broken thin ice is fatal.
  • Draft payload parity: thin ice and pushables must use dedicated fields (thinIceTiles, pushableRocks).

Local + Remote Drafts

  • Local snapshots are in-memory (this MCP process).
  • Remote drafts/publishing use Firebase (draftLevels, communityLevels, notifications).
  • Use preview_level for staging links and check_publish_readiness before publish_level.
  • Published level lifecycle tools are available via list_my_published_levels, get_my_published_level, unpublish_level, and restore_published_level.

Development

npm install
npm run build
npm test

License

MIT

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured