원격 Python 실행을 통해 Unreal Engine 인스턴스와 상호작용하기 위한 서버 구현입니다.
🚀 언리얼 인스턴스 관리
멀티캐스트를 통한 Unreal 노드 자동 검색
실시간 노드 상태 모니터링
LSP 호환 클라이언트를 통한 리소스 목록
💻 원격 실행
Unreal Engine 환경에서 Python 코드 실행
참석 및 무인 실행 모드 모두 지원
파일 실행 및 명령문 평가 모드
📊 로깅 및 모니터링
파일에 대한 자세한 로깅( mcp_unreal.log )
다양한 자세한 수준을 사용한 콘솔 로깅
노드 연결 상태 모니터링
Related MCP server: unreal-mcp
지엑스피1
RemoteExecutionConfig 에서 멀티캐스트 매개변수를 구성합니다.
# Default multicast settings (modify in server.py)
config.multicast_group_endpoint = ("239.0.0.1", 6766)server.py 에서 로깅 구성을 수정합니다:
# Adjust log levels
file_handler.setLevel(logging.DEBUG) # File logging
console_handler.setLevel(logging.INFO) # Console loggingpython -m src.mcp_server_unreal.server언리얼 인스턴스에 연결
{
"host": "239.0.0.1",
"port": 6766
}파이썬 코드 실행
{
"node_id": "<unreal-node-id>",
"code": "print('Hello Unreal')",
"unattended": true
}types.Resource(
uri="unreal://<node_id>",
name=f"Unreal Instance: {node_id}",
description="Unreal Engine instance",
mimeType="application/x-unreal"
)방법 | 설명 |
모드_실행_파일 | Python 파일 실행 |
모드 실행문 | Python 명령문 실행 |
모드 평가문 | Python 표현식 평가 |
일반적인 문제:
노드가 발견되지 않았습니다. MCP 플러그인을 사용하여 Unreal 인스턴스가 실행 중인지 확인하세요.
실행 시간 초과: 멀티캐스트 트래픽에 대한 방화벽 설정을 확인하세요.
연결이 끊어짐: 노드 상태 변경 사항을 확인하려면 mcp_unreal.log 모니터링하세요.
Apache-2.0 라이선스
在Unreal中执行Python代码
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | ||
| unattended | No |
| Name | Required | Description |
|---|---|---|
| error | No | |
| result | No | |
| success | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It states what the tool does but doesn't describe important behavioral aspects: whether this executes synchronously or asynchronously, what permissions are required, how errors are handled, whether it's safe for production use, or what the 'unattended' parameter actually means behaviorally. The description is minimal and lacks critical operational context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise - a single phrase that directly states the core function. There's zero wasted language, and it's perfectly front-loaded with the essential information. For its length, it's maximally efficient, though this conciseness comes at the cost of completeness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that this is a code execution tool with no annotations, 2 parameters (one with unclear purpose), and an output schema exists, the description is insufficient. While the output schema may describe return values, the description doesn't address safety concerns, execution context, error handling, or parameter meanings. For a potentially powerful/dangerous operation like code execution, more context is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate but doesn't. It mentions no parameters at all, leaving both 'code' and 'unattended' completely unexplained. The description doesn't clarify what format the Python code should be in, what execution environment is used, or what 'unattended' mode entails. This is inadequate for a tool with 2 parameters and 0% schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('执行Python代码' - execute Python code) and the context ('在Unreal中' - in Unreal). It specifies both verb and resource, though it doesn't need to distinguish from siblings since none exist. The purpose is unambiguous but could be more specific about what kind of Python execution this enables.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided about when to use this tool versus alternatives. The description doesn't mention prerequisites, limitations, or typical use cases. With no sibling tools, differentiation isn't needed, but basic usage context would help the agent understand appropriate scenarios for this operation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.0execute-pythonScored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool has a clear, distinct purpose of executing Python code within Unreal.
With only one tool, naming consistency is inherently perfect. The tool name 'execute-python' follows a clear verb_noun pattern, and there are no other tools to create inconsistency.
A single tool is too few for a server named 'MCP Unreal Server', which suggests a broader scope related to Unreal Engine. This minimal toolset feels thin and inadequate for typical Unreal automation or interaction tasks.
The tool surface is severely incomplete for an Unreal server. It only provides Python execution, lacking essential operations like asset management, level editing, blueprint interaction, or any other common Unreal Engine workflows, leaving significant gaps.
A comprehensive Model Context Protocol (MCP) server that enables AI assistants to control Unreal E…
Control Unreal Engine to browse assets, import content, and manage levels and sequences. Automate…
Hosted MCP server for task-first delegation to remote workstations and workers.
Remote MCP server exposing your Node Command fleet (files, shell, processes, browser) to AI hosts.