PyPI License: MIT Python 3.9+

Celeste map editor for AI agents — a Model Context Protocol (MCP) server that lets GitHub Copilot, Claude, and other MCP clients read, edit, analyze, procedurally generate, and preview Celeste .bin map files without ever opening Lönn.

Built for use with Everest mods. Works with maps created by Lönn or Ahorn.

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Features

60 MCP tools across 18 categories

Map Reading

Tool	Description
list_maps	List all .bin files in the project
read_map_overview	Summary of rooms, entities, triggers, and stylegrounds
read_room	Full detail for a single room: tiles, entities, triggers, decals
get_room_tiles	Raw tile grid (foreground or background) for a room

Map Reading Extensions (NEW in v5)

Tool	Description
read_map_metadata	Quick metadata (package, room count, world bounds) without full read
search_entities	Search entities across rooms by type, position, and room
search_triggers	Search triggers across rooms by type
compare_rooms	Side-by-side comparison of two rooms (size, difficulty, entities)

Map Editing

Tool	Description
add_entity	Place an entity in a room (auto-assigns ID)
remove_entity	Delete an entity by ID
add_trigger	Place a trigger (rectangular region) in a room, with optional path nodes
remove_trigger	Delete a trigger by ID
set_room_tiles	Replace the tile grid for a room
add_room	Create a new room with custom position/size
remove_room	Delete a room from the map
create_map	Create a new empty .bin map file

Map Editing Extensions (NEW in v5)

Tool	Description
update_entity	Update properties of an existing entity by ID
move_entity	Move an entity to a new position
update_room	Update room-level properties (music, dark, wind, etc.)
clone_room	Clone a room to a new name and position
batch_add_entities	Add multiple entities in one call (JSON array)
resize_room	Change room dimensions

Decals (NEW in v5)

Tool	Description
list_decals	List all decals in a room (FG or BG)
add_decal	Add a decal with texture, position, and scale
remove_decal	Remove a decal by index

Stylegrounds

Tool	Description
list_stylegrounds	List foreground + background effects (with indices)
add_styleground	Add an effect (parallax, custom Lua effect, apply group, etc.) to FG or BG
remove_styleground	Remove an effect by index
update_styleground	Merge property changes into an existing effect

Entity / Trigger Catalog

Tool	Description
list_entity_definitions	Browse Lönn entity .lua files in the project
get_entity_definition	Read the full source of a single entity definition
list_trigger_definitions	Browse Lönn trigger .lua files
list_effect_definitions	Browse Lönn effect .lua files

Catalog Extensions (NEW in v5)

Tool	Description
get_trigger_definition	Read the source of a trigger .lua file by name
get_effect_definition	Read the source of an effect .lua file by name

Analysis

Tool	Description
analyze_map	Statistics: entity counts, type breakdown, world bounds
visualize_map_layout	ASCII mini-map of room positions
preview_map_section	Detailed ASCII preview of a map region

Advanced Analysis — gdep-inspired (NEW in v5)

Tool	Description
analyze_entity_usage	Entity usage stats across the entire map
analyze_difficulty	Estimate room/map difficulty from hazards, nav aids, tile coverage
find_entity_references	Find all occurrences of an entity type across rooms
detect_map_patterns	Detect design archetypes (linear, hub, collectible-rich, etc.)
analyze_room_connectivity	Adjacency graph: isolated rooms, dead ends, hubs

Suggestions — gdep-inspired (NEW in v5)

Tool	Description
suggest_improvements	Actionable suggestions for a room (spawns, floors, balance)
compare_maps	Structural diff between two map files

Wiki / Cache — gdep-inspired (NEW in v5)

Tool	Description
wiki_save	Persist analysis results locally for instant repeated queries
wiki_search	Search cached wiki entries by key, content, or tags
wiki_list	List all wiki entries
wiki_get	Retrieve a specific wiki entry

Mod Project (NEW in v5)

Tool	Description
get_mod_info	Project info: everest.yaml, map count, PCG library, wiki
validate_map	Whole-map playability validation with optional auto-fix

Import / Export (NEW in v5)

Tool	Description
export_room_json	Export a room as JSON for external editing or sharing
import_room_json	Import a room from JSON into a map

Diff & Fix — gdep-inspired (NEW in v5)

Tool	Description
summarize_map_diff	Snapshot-based structural diffing for tracking map evolution
batch_validate_and_fix	Batch playability checks with optional auto-fix

Rendering

Tool	Description
render_map_html	Interactive HTML preview (zoom, pan, room details, minimap, search)

Procedural Generation

Tool	Description
build_pattern_library	Scan local .bin maps and extract room patterns into a reusable JSON library
generate_room_from_pattern	Generate a new room using patterns + a strategy + seed
validate_room	Check a room for playability issues (spawn, floor, bounds)
ingest_external_map	Download maps from external URLs (GameBanana etc.) and extract patterns

Image-to-Map & Terrain Generation (v4)

Tool	Description
generate_map_from_image	Convert a color-mapped image (PNG/JPG/BMP) into a full playable Celeste map
generate_terrain_map	Procedural map using seeded Perlin noise + Voronoi biomes
preview_terrain_biomes	ASCII preview of biome layout before generating

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Quick Start

1 — Install from PyPI

pip install loenn-mcp

Or clone and install from source:

git clone https://github.com/Maggy-Studio/loenn-mcp
cd loenn-mcp
pip install -e .

2 — Connect to GitHub Copilot (VS Code)

Add to your project's .vscode/mcp.json:

{
  "servers": {
    "loenn-mcp": {
      "type": "stdio",
      "command": "python",
      "args": ["-m", "loenn_mcp.server"],
      "env": {
        "LOENN_MCP_WORKSPACE": "${workspaceFolder}"
      }
    }
  }
}

Then ask Copilot things like:

• "What rooms are in 01_City_A.bin?"
• "Add a strawberry to room a-03 at position (120, 80)"
• "Render an HTML preview of 07_Hell_A.bin and open it"
• "Build a pattern library from all my maps, then generate 5 challenge rooms"

3 — Connect to Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "loenn-mcp": {
      "command": "python",
      "args": ["-m", "loenn_mcp.server"],
      "env": {
        "LOENN_MCP_WORKSPACE": "/absolute/path/to/your/mod"
      }
    }
  }
}

4 — One-click map preview (standalone)

python -m loenn_mcp.preview_map Maps/Maggy/Main/01_City_A.bin
python -m loenn_mcp.preview_map Maps/Maggy/Main/01_City_A.bin g-   # filter rooms by prefix

The HTML preview opens in your browser and supports:

• Scroll to zoom, drag to pan, pinch on touch
• Click a room → detail panel (entities, triggers, size)
• Live search filter (F to focus)
• Minimap with viewport indicator
• Keyboard shortcuts: + / - zoom, 0 fit, Esc deselect

---

Procedural Generation (PCG)

Generation strategies

Strategy	Description
balanced	Mix of exploration and challenge — good default
exploration	Open spaces, gentle platforming, few hazards
challenge	Dense tiles, many hazards, tight jumps
speedrun	Linear path, minimal platforms, fast flow

Model profiles

Profile	Seed behaviour	Best for
creative	Random seed each call	Maximum room variety
deterministic	Stable seed from strategy name	CI pipelines, reproducible layouts
architect	Random seed	Emphasis on room shape and connectivity

End-to-end pipeline

1. Build pattern library from existing maps
2. (Optional) ingest community maps from GameBanana
3. Create a blank map
4. Generate rooms with a chosen strategy and seed
5. Validate each room
6. Render HTML preview

Example agent prompts:

# Step 1 — build pattern library
build_pattern_library()

# Step 2 — ingest a GameBanana mod for richer pattern data
ingest_external_map(
  source_url="https://gamebanana.com/mods/53774",
  attribution="Spring Collab 2020 (various authors)",
  confirm_download=True,
  tags="community,collab"
)

# Step 3 — create map and generate rooms
create_map("Maps/PCG/MyAIMap.bin", "PCG/MyAIMap")
generate_room_from_pattern(
  map_path="Maps/PCG/MyAIMap.bin",
  room_name="a-01",
  strategy="exploration",
  seed=42,
  model_profile="deterministic"
)
generate_room_from_pattern(
  map_path="Maps/PCG/MyAIMap.bin",
  room_name="a-02",
  strategy="challenge",
  x=320
)

# Step 4 — validate
validate_room("Maps/PCG/MyAIMap.bin", "a-01")

# Step 5 — preview
render_map_html("Maps/PCG/MyAIMap.bin")

Reproducible generation (seeded)

Pass seed=<integer> and model_profile="deterministic" to get the exact same room every time:

# These two calls produce identical output:
generate_room_from_pattern(map_path="...", room_name="r1", strategy="challenge", seed=1234, model_profile="deterministic")
generate_room_from_pattern(map_path="...", room_name="r2", strategy="challenge", seed=1234, model_profile="deterministic")

GameBanana integration

ingest_external_map can fetch maps directly from GameBanana:

# Dry-run (no download) — shows what would happen
ingest_external_map(
  source_url="https://gamebanana.com/mods/53774",
  attribution="Spring Collab 2020",
  confirm_download=False
)

# Actual download + pattern extraction
ingest_external_map(
  source_url="https://gamebanana.com/mods/53774",
  attribution="Spring Collab 2020 (various authors, see mod page)",
  confirm_download=True,
  tags="expert,collab"
)

Downloaded files are saved to PCG/Datasets/ with an attribution.json file. Always verify the mod's licence permits derivative use before building on its patterns.

---

Image-to-Map Conversion (NEW in v4)

Convert any color-mapped image directly into a playable Celeste map. Each pixel becomes one 8×8 tile, with colors mapped to tile types and entities.

Default color mapping

Color	Hex	Maps to
Black	#000000	Solid tile (foreground)
White	#FFFFFF	Air (empty space)
Red	#FF0000	Spike hazard
Green	#00FF00	Player spawn
Blue	#0000FF	Jump-through platform
Yellow	#FFFF00	Strawberry collectible
Magenta	#FF00FF	Spring (bounce pad)
Cyan	#00FFFF	Refill crystal
Orange	#FF8000	Crumble block
Grey	#808080	Background solid (decorative)

Usage

# Basic — converts image using default color mapping
generate_map_from_image(image_path="Assets/my_level.png")

# Custom colors and scale
generate_map_from_image(
    image_path="Assets/large_map.png",
    output_path="Maps/Custom/level.bin",
    scale=4,  # 4×4 pixel blocks → 1 tile
    color_map_json='{"#FF0000":"solid","#00FF00":"spawn","#0000FF":"air"}'
)

How it works

1. The image is loaded and optionally downscaled by scale factor
2. Each pixel is matched to the closest color in the color map (within tolerance)
3. The grid is split into room-sized chunks (default 40×23 tiles = 320×184 px)
4. Each chunk becomes a room with proper tiles, entities, and a player spawn
5. The complete map is written as a .bin file

Requires Pillow — install with: pip install loenn-mcp[image]

---

Seeded Terrain Generation (NEW in v4)

Generate complete maps procedurally using Perlin noise and Voronoi diagrams, inspired by AliShazly/map-generator.

Biomes

Biome	Character	Terrain
mountain	Dense tiles	Tight platforms, spikes
forest	Moderate density	Many platforms, springs
plains	Open spaces	Gentle platforms, collectibles
lake	Sparse tiles	Jump-throughs, refills
cave	Enclosed	Crumble blocks, dark rooms
summit	Sparse platforms	Wind effects

Usage

generate_terrain_map(seed=42, difficulty=3)

Preview terrain biomes

preview_terrain_biomes(seed=42, width_rooms=4, height_rooms=3)

Output:

[P] [^] [^] [F]

[~] [P] [^] [M]

[C] [~] [P] [F]

### Generation algorithm

1. **Perlin noise** creates organic heightmap terrain — controls where solid tiles, platforms, and gaps appear
2. **Voronoi diagrams** partition the map into biome regions — each room inherits the biome of its Voronoi region
3. **Seeded RNG** ensures the same `seed` + parameters always produce the exact same output
4. **Difficulty scaling** (1-5) adjusts hazard density, tile coverage, and platform frequency
5. Biome properties control tile characters, entity types, room flags (dark, underwater, wind)

### Parameters

| Parameter | Default | Description |
|---|---|---|
| `seed` | -1 (random) | Integer seed for reproducible output |
| `width_rooms` | 4 | Rooms horizontally |
| `height_rooms` | 3 | Rooms vertically |
| `frequency` | 8.0 | Perlin noise frequency (lower = smoother) |
| `voronoi_points` | 12 | Number of biome region centres |
| `biome_set` | all | Comma-separated biome names |
| `difficulty` | 3 | 1-5 scale for hazard density |

---

## Game Analysis & Wiki (NEW in v5 — gdep-inspired)

Advanced analysis tools adapted from game design analysis patterns.

### Usage examples

```python
# Analyze difficulty across all rooms
analyze_difficulty(map_path="Maps/MyMod/1-City.bin")

# Detect gameplay patterns
detect_map_patterns(map_path="Maps/MyMod/1-City.bin")
# → "standard-level (7-15 rooms)", "linear-horizontal", "checkpointed (3 checkpoints)"

# Get suggestions for a room
suggest_improvements(map_path="Maps/MyMod/1-City.bin", room_name="lvl_a-01")

# Track map evolution with snapshots
summarize_map_diff(map_path="Maps/MyMod/1-City.bin")  # saves snapshot
# ... make edits ...
summarize_map_diff(map_path="Maps/MyMod/1-City.bin")  # shows diff

# Cache analysis results for instant re-use
wiki_save(key="city_difficulty", content="Avg difficulty 4.2/10, 3 hard rooms", tags="analysis")
wiki_search(query="difficulty")

# Batch validate and auto-fix
batch_validate_and_fix(map_path="Maps/MyMod/1-City.bin", auto_fix=True)

# Search for specific entities
search_entities(map_path="Maps/MyMod/1-City.bin", entity_type="strawberry")

# Clone and modify rooms
clone_room(map_path="Maps/MyMod/1-City.bin", source_room="lvl_a-01", new_name="lvl_a-01-copy")

# Export/import rooms as JSON
export_room_json(map_path="Maps/MyMod/1-City.bin", room_name="lvl_a-01")
import_room_json(map_path="Maps/MyMod/2-Resort.bin", json_path="Export/lvl_a-01.json")

Wiki cache

The wiki stores analysis results in .loenn_mcp_wiki/ as JSON files. Results persist across sessions so repeated queries return instantly.

---

AI-Powered Analysis (NEW — Claude API)

Leverage Anthropic's Claude AI for intelligent map design feedback, narrative generation, and entity placement suggestions.

Setup:

# Install with AI support (anthropic package included)
pip install loenn-mcp

# Set your API key (get from https://console.anthropic.com/)
$env:ANTHROPIC_API_KEY="sk-ant-api03-..."

Available Tools:

Tool	Description
ai_analyze_map	Claude-powered design feedback (general/difficulty/visual/flow analysis)
ai_describe_room	Generate narrative descriptions of rooms in various styles
ai_suggest_entities	Get specific entity placement recommendations with coordinates

Example prompts:

# Get AI feedback on your map design
ai_analyze_map(map_path="Maps/MyMod/1-City.bin", analysis_type="general")
# → "Strengths: Good checkpoint distribution. Suggestions: Add more strawberries in rooms 3-5..."

# Generate atmospheric room descriptions
ai_describe_room(map_path="Maps/MyMod/1-City.bin", room_name="lvl_a-03", style="atmospheric")
# → "A windswept precipice where ancient stone meets howling gales..."

# Get entity placement suggestions
ai_suggest_entities(map_path="Maps/MyMod/1-City.bin", room_name="lvl_a-03", goal="add_challenge")
# → "1. Add spikes at (120, 80) for a timing challenge..."

Analysis Types:

• general — Overall design assessment with improvement suggestions
• difficulty — Difficulty curve and balancing analysis
• visual — Visual variety and theme consistency feedback
• flow — Player movement flow and navigation clarity

Description Styles:

• atmospheric — Evocative, mood-focused descriptions
• technical — Gameplay-focused descriptions
• story — Narrative/story snippets
• brief — Concise 1-2 sentence summaries

Suggestion Goals:

• improve_flow — Better player guidance and navigation
• add_challenge — Skill-testing elements
• reduce_difficulty — Accessibility improvements
• add_secrets — Exploration rewards

The AI tools gracefully degrade if ANTHROPIC_API_KEY is not set, returning helpful error messages.

---

Environment Variables

Variable	Default	Description
LOENN_MCP_WORKSPACE	Current working directory	Root of your Celeste mod project. The server resolves all map paths relative to this. Path traversal outside the workspace is blocked.

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How It Works

celeste_bin.py — standalone binary parser

A pure-Python implementation of the Celeste .bin map format (no Everest or Lönn required):

• Full read/write round-trip with no data loss
• Handles all 7 value types: bool, uint8, int16, int32, float32, lookup string, raw string, RLE-encoded string
• Recursive element tree matching the internal Lönn/Maple format

pcg.py — procedural generation module

Provides:

• Pattern extraction — converts .bin rooms into reusable pattern records (size class, entity density, tile motifs, trigger usage, gameplay tags)
• Pattern library — JSON-based store with deduplication by content hash
• Strategy-based generation — balanced, exploration, challenge, speedrun modes
• Seeded randomness — random.Random(seed) for reproducible outputs; seed exposed via MCP tool parameters
• Model profiles — deterministic / creative / architect profiles control how seeds are resolved

image_map.py — image-to-map conversion (NEW in v4)

Converts color-mapped images into playable Celeste maps:

• Color-to-role mapping — configurable palette mapping colors to tiles and entities
• Automatic room splitting — large images are divided into room-sized chunks
• Entity placement — spawns, hazards, collectibles extracted directly from pixel colors
• Scale support — large images can be downscaled (N×N pixel blocks → 1 tile)
• Tolerance matching — fuzzy color matching for hand-drawn or anti-aliased images

terrain_gen.py — seeded terrain generator (NEW in v4)

Procedural map generation inspired by AliShazly/map-generator:

• Perlin noise — pure-Python implementation with fractal octaves for organic terrain
• Voronoi biomes — map partitioned into distinct biome regions (mountain, forest, plains, lake, cave, summit)
• Fully seeded — same seed + parameters = identical output every time
• Difficulty scaling — 1-5 scale controls hazard density, tile coverage, and platform frequency
• Biome-aware entities — each biome has appropriate hazards, collectibles, and room flags

gdep_tools.py — game analysis tools (NEW in v5)

Integrates game analysis concepts from pirua-game/ai_game_base_analysis_cli_mcp_tool (gdep):

• Wiki caching — persist analysis results locally so repeated queries are instant (.loenn_mcp_wiki/)
• Pattern detection — detect gameplay design archetypes (linear progression, hub layouts, collectible-rich, wind corridors)
• Difficulty analysis — estimate room/map difficulty from hazard density, navigation aids, tile coverage (1-10 scale)
• Room connectivity — adjacency graph analysis showing isolated rooms, dead ends, and hubs
• Map diffing — snapshot-based structural diffing for tracking map evolution over time
• Batch validation — whole-map playability checks (spawns, floors, bounds) with optional auto-fix
• Suggestions — actionable improvement suggestions based on room analysis

ai_analyzer.py — AI-powered analysis (NEW)

Integrates Anthropic's Claude API for intelligent map design assistance:

• Map analysis — AI-powered feedback on design, difficulty, visuals, and flow
• Room descriptions — Generate narrative descriptions in atmospheric, technical, story, or brief styles
• Entity suggestions — Get specific entity placement recommendations with coordinates
• Graceful degradation — Helpful error messages when ANTHROPIC_API_KEY is not configured

server.py — MCP server

Built with FastMCP. All file paths are resolved relative to LOENN_MCP_WORKSPACE with path-traversal protection. Map writes are atomic (parse → mutate → write). External downloads require explicit confirm_download=True.

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Requirements

• Python 3.9+
• fastmcp >= 3.0.0
• anthropic >= 0.40.0 (optional — only needed for AI-powered tools)
• Pillow >= 9.0 (optional — only needed for generate_map_from_image)

Install with all optional features: pip install loenn-mcp[image]

No Celeste installation required to parse, generate, or preview maps.

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License

MIT — see LICENSE.