Escape Room MCP
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Escape Room MCP Play the escape room."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Escape Room MCP
An MCP server that turns any LLM into an escape room player.
The model wakes up inside The Clockmaker's Study. It can only perceive and change the room through MCP tools, while the server owns the authoritative game state, puzzle progression, inventory, timer, and score.
Demo
The visual is adapted from deterministic responses from the server. A Claude Desktop recording can be dropped into
assets/demo.gif and linked here without changing the game.
Related MCP server: mcp-llm
Why this exists
Demonstrate an agent discovering information, chaining tools, making mistakes, and recovering.
Provide a compact benchmark based on tool calls, elapsed time, hints, and final score.
Show why authoritative state belongs in the environment rather than in a model's conversational memory.
Offer a complete stateful MCP example without APIs, credentials, databases, or deployment infrastructure.
How it plays
The room contains a locked door, a silent grandfather clock, a bookshelf, a portrait, a desk, a bricked-up window, and a suspiciously ordinary rug. The player explores, inspects details, manipulates discovered items, and tries to open the door.
The server instructions and tool descriptions contain no solution. A capable model can discover the intended puzzle chain using only the returned narrative, and an observant model may find a legitimate shortcut.
Setup
Requirements
Python 3.11 or newer
An MCP host such as Claude Desktop, Claude Code, or MCP Inspector
Install the locked environment from the project directory:
uv sync --frozenClaude Desktop
Open claude_desktop_config.json:
Windows:
%APPDATA%\Claude\claude_desktop_config.jsonmacOS:
~/Library/Application Support/Claude/claude_desktop_config.json
Add the server, replacing the directory with an absolute path:
{
"mcpServers": {
"escape-room": {
"command": "uv",
"args": [
"--directory",
"/absolute/path/to/escape-room-mcp",
"run",
"escape-room-mcp"
]
}
}
}On Windows, use either forward slashes or escaped backslashes in the JSON path. If Claude Desktop does not
inherit your shell's PATH, set command to the absolute path returned by where.exe uv.
Quit and restart Claude Desktop after changing the configuration. Then ask it:
Play the escape room. Do not ask me for help unless you run out of hints.
MCP Inspector
Launch the server directly in the Inspector UI:
npx -y @modelcontextprotocol/inspector uv --directory /absolute/path/to/escape-room-mcp run escape-room-mcpThe server uses stdio, so running uv run escape-room-mcp by itself correctly waits for an MCP client and
does not print ordinary application output to stdout.
Tools
Tool | Purpose |
| Create an isolated session and enter the room |
| Survey currently visible room features |
| Examine one object in detail |
| Try to take or pull a discovered item |
| Review currently held items |
| Use an inventory item on a room object |
| Try a four-digit string on the door keypad |
| Request one of at most three state-aware hints |
| Check time, calls, hints, and current score |
| Reset one session without creating a new ID |
Object and item names accept deterministic aliases such as clock for grandfather clock. Failed in-world
actions return narrative results instead of protocol failures, allowing the model to reason and recover.
Scoring
Each game starts at 1,000 points.
Event | Score effect |
Action call ( | -10 |
Hint delivered | -100 |
Each whole minute below 15 at escape | +5, up to +75 |
Failed action attempts still cost 10 points. look_around, status, and hint do not carry the action penalty,
but every valid tool invocation counts toward the final tool-call total.
Tool calls | Title |
10 or fewer | Master Escapist |
11 to 20 | Clever Detective |
More than 20 | Persistent Survivor |
Benchmark
Run each model in a fresh conversation with no room spoilers. Give every model the same prompt, permit autonomous tool use, and copy the metrics from the victory response. Record the exact model version and client because tool selection behavior can differ between hosts.
Model | Client | Tool calls | Time | Hints | Score |
Claude | Claude Desktop | TBD | TBD | TBD | TBD |
GPT | MCP-capable client | TBD | TBD | TBD | TBD |
Gemini | MCP-capable client | TBD | TBD | TBD | TBD |
Suggested benchmark prompt:
You are the player. Escape the room autonomously using only the available tools. Report the final result.
Architecture
MCP host
| stdio / MCP
v
FastMCP tool adapter
|
v
GameManager
+-- session A: stage, inventory, metrics, timer
+-- session B: stage, inventory, metrics, timer
+-- session C: stage, inventory, metrics, timerThe model does not own puzzle state. After start_game, every subsequent call includes an opaque session_id, and
GameManager resolves that ID to an in-memory session under a lock. This prevents conversational omissions,
retries, or hallucinated inventory from changing reality. It also allows multiple isolated games in one server
process.
The tradeoff is intentional: sessions disappear when the local server process exits. There is no persistence, expiry policy, analytics service, network transport, or embedded LLM in v1.
Development
uv sync --all-groups
uv run ruff format --check .
uv run ruff check .
uv run pytest --cov=escape_room_mcp --cov-report=term-missing --cov-fail-under=95
uv buildThe tests cover the intended chain, direct shortcut, error recovery, aliases, scoring boundaries, hint states,
session isolation, protocol schemas, in-memory MCP calls, and a packaged stdio subprocess.
License
Available Tools
10 toolsenter_codeEnter Door CodeC
Enter a candidate four-digit code on the locked door's keypad.
Inputs: code (required), session_id (required).
hintRequest HintA
Request a state-aware hint; at most three hints are available per game.
Inputs: session_id (required).
inspectInspect Room ObjectA
Examine one visible room object in greater detail.
Inputs: target (required), session_id (required).
inventoryCheck InventoryA
Review the items you are currently carrying.
Inputs: session_id (required).
look_aroundLook AroundB
Survey the room and its currently visible features.
Inputs: session_id (required).
pick_upPick Up ItemA
Try to take or pull an item you have discovered in the room.
Inputs: item (required), session_id (required).
restartRestart Escape RoomADestructive
Discard progress and restart this session from the beginning.
Inputs: session_id (required).
start_gameStart Escape RoomA
Start a fresh escape-room session and receive its session ID and introduction.
Inputs: player_name.
statusCheck Game StatusA
Check elapsed time, tool calls, hints used, and current score.
Inputs: session_id (required).
useUse ItemB
Use an item from your inventory on a room object.
Inputs: item (required), target (required), session_id (required).
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 10 tools
Each tool has a unique and well-defined purpose, from starting the game to interacting with objects and requesting hints, with no overlap between them.
Most tools follow a verb_noun pattern (e.g., start_game, enter_code), but a few like inventory, status, and hint are nouns, creating slight inconsistency while remaining understandable.
With 10 tools, the set is well-scoped for an escape room game, covering essential actions without being too many or too few.
The tool surface covers all major escape room interactions: starting, exploring, collecting, using items, entering codes, getting hints, and checking status, with no obvious gaps.
Maintenance
Related MCP Connectors
MCP server for AI dialogue using various LLM models via AceDataCloud
An MCP server that gives your AI access to the source code and docs of all public github repos
Hosted YouTube MCP server for Claude, ChatGPT and Cursor, with each action switched per account.
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