loenn-mcp

A Celeste map editor for AI agents — a Model Context Protocol (MCP) server that brings full Celeste .bin map editing to Claude, GitHub Copilot, and other MCP clients, plus a standalone pcgscene CLI for scripting and CI. Read, edit, analyze, generate, and preview maps without opening Lönn.

"For AI agents" describes who talks to this server (any MCP client), not what runs inside it — every tool here is deterministic and procedural. See No AI Inside.

Works with Everest mods and maps from Lönn or Ahorn.

Contents

• Features

• Installation & setup

• pcgscene CLI

• Procedural generation

• Image-to-map conversion

• Seeded terrain generation

• Analysis & insights

• No AI inside (v7.0.0)

• Configuration

• Architecture

• Requirements

• License

Related MCP server: onyx MCP

Features

75 MCP tools for complete map manipulation, plus the pcgscene command-line tool for scripting and CI.

Core Tools

Reading & Querying

• list_maps — List all .bin files

• read_map_overview — Summary of rooms, entities, triggers, stylegrounds

• read_room — Full room details (tiles, entities, triggers, decals)

• get_room_tiles — Raw tile grid (FG or BG)

• read_map_metadata — Quick metadata without full read

• search_entities — Find entities by type, position, room

• search_triggers — Find triggers by type

• compare_rooms — Side-by-side room comparison

Editing

• add_entity / remove_entity — Place or delete entities

• update_entity / move_entity — Modify entity properties or position

• add_trigger / remove_trigger — Place or delete triggers

• set_room_tiles — Replace tile grid

• add_room / remove_room — Create or delete rooms

• create_map — Create new .bin file

• update_room — Modify room properties (music, dark, wind, etc.)

• clone_room — Duplicate a room

• batch_add_entities — Add multiple entities at once

• resize_room — Change room dimensions

Decals & Effects

• list_decals / add_decal / remove_decal — Manage foreground/background decals

• list_stylegrounds / add_styleground / update_styleground / remove_styleground — Manage map effects

Definitions & Catalog

• list_entity_definitions / get_entity_definition — Browse entity types

• list_trigger_definitions / get_trigger_definition — Browse trigger types

• list_effect_definitions / get_effect_definition — Browse effect types

Analysis & Insights

Basic Analysis

• analyze_map — Entity counts, type breakdown, world bounds

• visualize_map_layout — ASCII mini-map

• preview_map_section — Detailed ASCII preview

Advanced Analysis

• analyze_entity_usage — Entity stats across entire map

• analyze_difficulty — Room/map difficulty estimation

• find_entity_references — Locate all instances of an entity type

• detect_map_patterns — Identify design archetypes (linear, hub, etc.)

• analyze_room_connectivity — Adjacency graph analysis

Suggestions & Improvements

• suggest_improvements — Actionable room suggestions

• compare_maps — Structural diff between maps

Wiki & Caching

• wiki_save / wiki_search / wiki_list / wiki_get — Persist and retrieve analysis results

Project Management

• get_mod_info — Project metadata and structure

• validate_map / batch_validate_and_fix — Playability validation with auto-fix

• export_room_json / import_room_json — JSON room exchange

Diffing

• summarize_map_diff — Track map evolution with snapshots

Rendering

• render_map_html — Interactive HTML preview (zoom, pan, search, minimap)

Procedural Generation

Pattern-Based Generation

• build_pattern_library — Extract patterns from existing maps

• generate_room_from_pattern — Generate rooms with strategy + seed

• ingest_external_map — Download and extract patterns from GameBanana

Image & Terrain Generation

• generate_map_from_image — Convert color-mapped images to playable maps

• generate_terrain_map — Procedural maps with Perlin noise + Voronoi biomes

• preview_terrain_biomes — Preview biome layout before generation

Installation & Setup

Install from PyPI

pip install loenn-mcp

Or from source:

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

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"
      }
    }
  }
}

Connect to GitHub Copilot (VS Code)

Add to .vscode/mcp.json:

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

Preview Maps Locally

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

The interactive HTML preview supports zoom, pan, room details, search, and minimap with keyboard shortcuts.

pcgscene CLI

A batch-first command-line tool for scanning and repairing maps outside an MCP client — install once, use from scripts, pre-commit hooks, or CI. Every command that touches a .bin file writes through the same atomic, backed-up, round-trip-validated path as the MCP server, so a bad run can never destroy a map.

pip install loenn-mcp
pcgscene scan MyMap.bin              # score every room + map connectivity
pcgscene validate MyMap.bin          # in-game readiness checklist (exit 0/1)
pcgscene fix MyMap.bin --dry-run     # preview safe auto-repairs
pcgscene fix MyMap.bin               # apply them (backed up first)
pcgscene diff before.bin after.bin   # what changed between two maps

Every command accepts --json (and --out FILE) for machine-readable output, so an editor plugin or CI step can consume the report directly.

Procedural Generation

Generation Strategies

Model Profiles

Quick Start Example

# 1. Build pattern library from existing maps
build_pattern_library()

# 2. Create a new map
create_map("Maps/PCG/Generated.bin", "PCG/Generated")

# 3. Generate rooms
generate_room_from_pattern(
  map_path="Maps/PCG/Generated.bin",
  room_name="a-01",
  strategy="exploration",
  seed=42,
  model_profile="deterministic"
)

# 4. Validate and preview
validate_room("Maps/PCG/Generated.bin", "a-01")
render_map_html("Maps/PCG/Generated.bin")

Seeded Generation

Use seed=<int> + model_profile="deterministic" for reproducible output:

# Both calls produce identical rooms
generate_room_from_pattern(..., strategy="challenge", seed=1234, model_profile="deterministic")
generate_room_from_pattern(..., strategy="challenge", seed=1234, model_profile="deterministic")

GameBanana Integration

Download and extract patterns from community mods:

# Dry-run (preview only)
ingest_external_map(
  source_url="https://gamebanana.com/mods/53774",
  attribution="Spring Collab 2020",
  confirm_download=False
)

# Download and extract
ingest_external_map(
  source_url="https://gamebanana.com/mods/53774",
  attribution="Spring Collab 2020 (various authors)",
  confirm_download=True,
  tags="community,expert"
)

Patterns are saved to PCG/Datasets/ with attribution. Always verify mod licenses permit derivative use.

Image-to-Map Conversion

Convert color-mapped images directly into playable Celeste maps. Each pixel becomes one 8×8 tile.

Default Color Mapping

Usage

# Basic conversion
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"}'
)

Requires Pillow: pip install loenn-mcp[image]

Seeded Terrain Generation

Generate complete maps with Perlin noise and Voronoi biomes. Inspired by AliShazly/map-generator.

Biomes

Quick Example

# Generate a 4×3 map with seed 42
generate_terrain_map(seed=42, difficulty=3, width_rooms=4, height_rooms=3)

# Preview biome layout before generating
preview_terrain_biomes(seed=42, width_rooms=4, height_rooms=3)
# Output:
# [P] [^] [^] [F]
# [~] [P] [^] [M]
# [C] [~] [P] [F]

Parameters

Analysis & Insights

Advanced analysis tools for map design, difficulty, and patterns.

Quick Examples

# Analyze difficulty
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"

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

# Track map evolution
summarize_map_diff(map_path="Maps/MyMod/1-City.bin")  # save snapshot
# ... edit map ...
summarize_map_diff(map_path="Maps/MyMod/1-City.bin")  # show diff

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

# Batch validation
batch_validate_and_fix(map_path="Maps/MyMod/1-City.bin", auto_fix=True)

# Search and clone
search_entities(map_path="Maps/MyMod/1-City.bin", entity_type="strawberry")
clone_room(map_path="Maps/MyMod/1-City.bin", source_room="lvl_a-01", new_name="lvl_a-01-copy")

# Export/import rooms
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

Analysis results persist in .loenn_mcp_wiki/ as JSON files for instant re-use across sessions.

No AI Inside (v7.0.0)

As of v7.0.0 every tool in loenn-mcp is deterministic and procedural — Markov chains, wave-function collapse, noise, BFS/graph analysis. The server makes no calls to any AI/LLM API, requires no API key, and ships no AI models. The former ai_analyze_map / ai_describe_room / ai_suggest_entities tools and the anthropic dependency were removed.

Disclaimer: this project was developed with the assistance of Claude (Anthropic) as a coding tool, based on the algorithms published in Robinet et al., "Towards a Celeste AI Framework" (FDG '25, DOI 10.1145/3723498.3723796). All output has been human-tested in Lönn and in-game.

Configuration

Architecture

Core Modules

celeste_bin.py — Standalone .bin parser

• Pure Python (no Everest/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 Lönn/Maple format

pcg.py — Procedural generation

• Pattern extraction from rooms (size, entity density, tile motifs, gameplay tags)

• JSON pattern library with deduplication

• Strategy-based generation (balanced, exploration, challenge, speedrun)

• Seeded randomness for reproducible output

• Model profiles (deterministic, creative, architect)

image_map.py — Image-to-map conversion

• Color-to-role mapping (configurable palette)

• Automatic room splitting

• Entity placement from pixel colors

• Scale support (downscaling)

• Fuzzy color matching

terrain_gen.py — Seeded terrain generation

• Perlin noise with fractal octaves

• Voronoi biome partitioning

• Fully seeded (same seed = identical output)

• Difficulty scaling (1-5)

• Biome-aware entities

gdep_tools.py — Game analysis

• Wiki caching (.loenn_mcp_wiki/)

• Pattern detection (linear, hub, collectible-rich, etc.)

• Difficulty analysis (1-10 scale)

• Room connectivity graphs

• Map diffing with snapshots

• Batch validation and auto-fix

• Actionable suggestions

server.py — MCP server

• Built with FastMCP

• Path-traversal protection

• Atomic map writes

• Explicit download confirmation

cli.py — pcgscene command-line tool

• scan / score / validate / fix / diff, all --json-capable

• Shares celeste_bin's atomic-write path — no separate write logic to drift

Requirements

• Python 3.9+

• fastmcp >= 3.0.0

• Pillow >= 9.0 (optional, for image-to-map conversion)

Install with all optional features:

pip install loenn-mcp[image]

No Celeste installation required.

License

MIT — see LICENSE.