UnityLudometryMCP MCP server
Commit Oct 14, 2026Enables AI assistants to analyze Unity games and build mods through static analysis and a runtime agent.
Also known as RectangleEquals/UnityLudometryMCP
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What is UnityLudometryMCP MCP?
UnityLudometryMCP enables AI assistants to analyze Unity games and build mods through static analysis and a runtime agent.
Summary generated from the project's README
About UnityLudometryMCP MCP server
Analyzing Unity games and developing mods through an AI assistant is the primary function of UnityLudometryMCP. This local MCP server acts as an orchestrator that combines static analysis tools, online research, and a runtime agent known as the UnityRuntimeAnalysisAgent into a single knowledge base. It is designed for developers who want to query game data and perform modifications with high levels of oversight.
Users run the server on Windows using Python 3.13 and the .NET SDK. While the current pre-release version primarily offers the server_status tool, the architecture supports future integration with dnSpyEx and AssetStudio for deeper static analysis. The system operates via stdio and requires the user to provide explicit consent for any actions that modify a running game or write files. The in-game agent begins in a read-only state and includes an E-STOP feature to halt all automated activity immediately.
What you can do with it
- 1Verify server connection statusCheck if the MCP server is correctly registered and running within your AI client.
- 2Perform runtime game analysisUse a runtime agent inside a Unity game to gather data for an AI assistant to query.
Written from the project's README. Every use case is checked against a quote from it.
README
View on GitHubUnityLudometryMCP
A local Model Context Protocol (MCP) server that helps you understand Unity games and build mods for them. It combines optional static-analysis tools, online research and a runtime agent inside the game (UnityRuntimeAnalysisAgent) into one knowledge base that your AI assistant can query and act on, with your consent at every step that changes anything.
Status: pre-release (v0.1 in development). The MCP server runs and can be registered with your client, but it offers only
server_statusso far. The rest of the orchestrator is being built.
What's here now
- The MCP server
unity-ludometry-mcp(stdio) with theserver_statustool. protocol/: the schema-first protocol spoken between the orchestrator and the agent: JSON Schemas for every method, event and exchanged file, and the zero-dependency C# packageUnityLudometry.Protocol(with message types generated from the schemas) that the agent uses.src/unity_ludometry_mcp/: the Python orchestrator: the protocol layer (models generated from the schemas, strict JSON, framing, envelopes, named-pipe/TCP transports), agent discovery, the agent client, and the package layout the rest of the orchestrator grows into.docs/: documentation.
Requirements
Windows, Python 3.13 with uv, and the .NET SDK (10.0 or newer). Later, optionally: dnSpyEx and the AssetStudio command-line tool, for static analysis.
Install and register
There are no releases yet; run the server from a clone and register it with your MCP client as unity-ludometry-mcp.
In Claude Code:
git clone https://github.com/RectangleEquals/UnityLudometryMCP
cd UnityLudometryMCP
uv sync
claude mcp add unity-ludometry-mcp -- uv run --directory <path to your clone> unity-ludometry-mcp
Getting started has the JSON form for other clients, what gets written where (the profile root, otherwise only folders you choose; today nothing at all) and how to remove everything. Configuration lists the environment variables.
Building
See CONTRIBUTING. In short: uv sync --group dev.
About the use of AI in this project
This section is here so you can decide for yourself, with accurate information.
How this project is being made. The design and implementation are produced with the help of an AI coding assistant (Anthropic's Claude), working under the direct supervision of a human developer. In practice:
- The human decides what the project is for, sets every requirement and constraint, and chooses between the options the AI proposes.
- The AI drafts code and documentation within those requirements, one small, reviewable step at a time.
- The human reviews every step before it becomes part of the project. Commits are made by the human, or by the AI only with the human's explicit permission.
- Behaviour is verified during development (for the protocol: schema validation and example exchanges replayed by both sides) and, as the project grows, by running it against real Unity games.
How this project uses AI when you run it. UnityLudometryMCP is an MCP server: it contains no AI model and never contacts an AI service by itself. It offers tools to the AI assistant you connect it to (for example Claude Code), and that assistant decides which tools to call. You stay in control:
- Anything that changes something (installing the in-game agent, raising its permission level, modifying a running game, writing files) needs your explicit consent, and every installation is recorded so it can be undone exactly.
- The in-game agent starts read-only, lists every action in its overlay, and has an E-STOP that stops all automated activity immediately.
- Files are written only to locations you choose. Online research is done by your assistant's own web tools, and its results are treated as unverified until the game confirms them.
If you have questions or concerns about any of this, please open an issue.
License
MIT. See LICENSE.
UnityLudometryMCP MCP questions
What is the current development status of UnityLudometryMCP?
The project is in a pre-release state (v0.1) and currently only offers the server_status tool.
Does the server include its own AI model?
No, the server does not contain an AI model and does not contact AI services directly; it provides tools for your connected AI assistant to use.
How can I stop automated activity in the game?
The in-game agent includes an E-STOP feature that immediately halts all automated activity.