{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-ivanmurzak--unity-mcp","slug":"ivanmurzak--unity-mcp","name":"Unity-MCP","type":"mcp","url":"https://ai-game.dev","page_url":"https://unfragile.ai/ivanmurzak--unity-mcp","categories":["mcp-servers","code-editors","app-builders","testing-quality"],"tags":["ai","ai-integration","anthropic","claude","claude-code","claude-desktop","cli","copilot","cursor","game-development","gamedev","gemini","llm","mcp","mcp-server","model-context-protocol","openai","unity","windsurf"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-ivanmurzak--unity-mcp__cap_0","uri":"capability://tool.use.integration.reflection.based.c.method.to.mcp.tool.conversion","name":"reflection-based c# method-to-mcp-tool conversion","description":"Automatically exposes any C# method as an MCP tool through a single-line attribute decorator, using .NET reflection to introspect method signatures, parameters, and return types at runtime. The system dynamically generates tool schemas compatible with Claude, Gemini, and OpenAI function-calling APIs without manual schema definition. This enables game developers to expose custom gameplay logic, editor utilities, or game systems directly to AI clients without boilerplate tool registration code.","intents":["I want to let Claude call my custom C# methods without writing tool schemas manually","I need to expose game logic functions to AI agents with minimal code overhead","I want to turn existing utility methods into AI-accessible tools with one decorator"],"best_for":["Unity game developers building AI-assisted game development workflows","Teams integrating AI agents into existing C# codebases","Developers prototyping AI-driven gameplay mechanics"],"limitations":["Reflection-based approach adds ~50-100ms overhead per tool discovery at startup","Complex generic types or custom serialization may require manual schema overrides","Only supports methods with serializable parameter and return types (primitives, strings, objects with public properties)"],"requires":["Unity 2022.3.61f1 or later","C# 9.0+ for attribute syntax","Unity-MCP Plugin installed in project"],"input_types":["C# method signatures with primitive/serializable parameters"],"output_types":["MCP tool schema (JSON)","Function call results (serialized to JSON)"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ivanmurzak--unity-mcp__cap_1","uri":"capability://tool.use.integration.asp.net.core.mcp.server.with.signalr.bidirectional.communication","name":"asp.net core mcp server with signalr bidirectional communication","description":"Implements the Model Context Protocol (MCP) as an ASP.NET Core server that communicates with AI clients (Claude Code, Gemini, Copilot, Cursor) via SignalR WebSocket connections. The server translates MCP protocol messages into Unity Editor API calls and streams responses back asynchronously. This architecture enables real-time, low-latency interaction between AI clients and the Unity Editor running on a developer's machine, with automatic server binary management and multi-platform support (Windows, macOS, Linux).","intents":["I want Claude Code to directly control my Unity Editor and run commands in real-time","I need a persistent connection between my AI client and Unity that handles reconnection gracefully","I want to use any MCP-compatible AI client (Claude, Gemini, Copilot) with my Unity project"],"best_for":["Game developers using Claude Code, Cursor, or Windsurf as their primary IDE","Teams building AI-assisted game development pipelines","Developers who want vendor-agnostic AI integration (works with any MCP client)"],"limitations":["Requires ASP.NET Core runtime (~100MB footprint) — not suitable for minimal embedded scenarios","SignalR WebSocket communication adds ~20-50ms latency per round-trip compared to direct in-process calls","Server binary must be running separately from Unity Editor — adds process management complexity","No built-in authentication — assumes trusted local network only"],"requires":[".NET 6.0 or later runtime","ASP.NET Core 6.0+","Windows, macOS, or Linux OS","Open port for SignalR (default 5000)"],"input_types":["MCP protocol messages (JSON-RPC)","Tool call requests with parameters"],"output_types":["MCP protocol responses (JSON-RPC)","Tool execution results","Streaming resource updates"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ivanmurzak--unity-mcp__cap_10","uri":"capability://tool.use.integration.custom.tool.development.framework.with.attribute.driven.registration","name":"custom tool development framework with attribute-driven registration","description":"Provides a framework for developers to create custom MCP tools by implementing C# methods with `[MCPTool]` attributes, with automatic schema generation, parameter validation, and error handling. The framework handles serialization/deserialization of complex types, supports async methods, and integrates with the MCP protocol automatically. Developers focus on business logic while the framework handles protocol compliance and client communication.","intents":["I want to create custom tools for my game-specific AI workflows","I need to expose internal game systems to AI clients without writing MCP protocol code","I want to add validation and error handling to AI-callable methods"],"best_for":["Game developers building domain-specific AI tools","Teams extending Unity-MCP with custom functionality","Developers who want to avoid MCP protocol implementation details"],"limitations":["Attribute-based approach requires understanding of reflection and serialization","Complex type marshalling may require custom serializers","Async method support adds complexity to error handling and timeouts","No built-in rate limiting or quota management per tool"],"requires":["C# 9.0+ for attribute syntax","Understanding of .NET reflection and serialization","Unity-MCP Plugin installed"],"input_types":["C# method implementations","Attribute decorators"],"output_types":["MCP tool schema","Tool execution results"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ivanmurzak--unity-mcp__cap_11","uri":"capability://tool.use.integration.cross.ide.compatibility.layer.for.claude.code.cursor.windsurf.and.copilot","name":"cross-ide compatibility layer for claude code, cursor, windsurf, and copilot","description":"Abstracts IDE-specific MCP client implementations (Claude Code, Cursor, Windsurf, VS Code with Copilot) through a unified server interface that works identically across all clients. The system handles IDE-specific configuration formats, authentication mechanisms, and UI integration points. Developers configure Unity-MCP once and it works seamlessly with any MCP-compatible IDE without additional setup.","intents":["I want to use Claude Code, Cursor, and Windsurf interchangeably with the same Unity-MCP setup","I need my AI tools to work regardless of which IDE my team members use","I want to avoid IDE-specific configuration and setup"],"best_for":["Teams with heterogeneous IDE preferences","Organizations standardizing on MCP but allowing IDE choice","Developers who switch between IDEs and want consistent AI integration"],"limitations":["IDE-specific features (inline suggestions, UI panels) are not available across all clients","Some IDEs may have different MCP protocol compliance levels","Configuration discovery may fail if IDE-specific config files are missing","No unified UI — each IDE has its own MCP client UI"],"requires":["MCP-compatible IDE (Claude Code, Cursor, Windsurf, VS Code 1.80+)","Proper MCP configuration in IDE settings"],"input_types":["IDE-specific MCP configuration"],"output_types":["Unified MCP server interface"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ivanmurzak--unity-mcp__cap_12","uri":"capability://memory.knowledge.efficient.token.usage.optimization.through.context.pruning.and.caching","name":"efficient token usage optimization through context pruning and caching","description":"Implements strategies to minimize LLM token consumption by selectively exposing only relevant scene data, caching frequently-accessed resources, and pruning large hierarchies based on AI client interest. The system can filter GameObject hierarchy to show only relevant branches, compress asset metadata, and cache scene snapshots to avoid re-serializing unchanged data. This reduces token costs for long-running AI sessions and enables more complex reasoning within token budgets.","intents":["I want to reduce token usage when working with large scenes","I need Claude to focus on relevant GameObjects without seeing the entire hierarchy","I want to cache scene state to avoid re-serializing unchanged data"],"best_for":["Teams with large, complex scenes where token costs are significant","Long-running AI sessions where context efficiency matters","Cost-conscious organizations using expensive LLM providers"],"limitations":["Pruning logic may cause AI to miss relevant context if filters are too aggressive","Caching adds complexity to state management — stale cache can cause incorrect AI decisions","Selective exposure requires manual configuration of what data is relevant","Token savings are project-specific — benefits vary widely based on scene complexity"],"requires":["Configuration specifying which scene data to expose","Understanding of token counting for your LLM provider"],"input_types":["Scene hierarchy","Pruning/filtering rules"],"output_types":["Optimized context (pruned hierarchy, compressed metadata)","Token usage metrics"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ivanmurzak--unity-mcp__cap_2","uri":"capability://memory.knowledge.dynamic.gameobject.hierarchy.introspection.and.manipulation","name":"dynamic gameobject hierarchy introspection and manipulation","description":"Exposes the Unity GameObject hierarchy as queryable resources through the MCP resource API, allowing AI clients to inspect scene structure, component composition, and property values in real-time. Implements reflection-based component property reading and writing, enabling AI agents to understand the current game state and modify GameObjects, components, and their properties through natural language commands. The system maintains a live view of the hierarchy that updates as the scene changes.","intents":["I want Claude to understand the current scene structure and tell me what GameObjects and components exist","I need AI to modify GameObject properties, add/remove components, or restructure the hierarchy based on my description","I want to query the scene state (positions, rotations, active status) and have AI reason about it"],"best_for":["Game designers using AI to prototype level layouts and GameObject configurations","Developers building AI-assisted scene editors","Teams automating GameObject setup and configuration through natural language"],"limitations":["Reflection-based property access is slower than direct API calls — ~5-10ms per component introspection","Large scenes (1000+ GameObjects) may cause noticeable latency when serializing full hierarchy","Only exposes public properties — private fields and serialized private fields are not accessible","No undo/redo integration — AI modifications bypass Unity's undo system"],"requires":["Unity 2022.3.61f1 or later","Unity-MCP Plugin with GameObject Tools enabled","Active Unity scene loaded in Editor"],"input_types":["GameObject path queries (e.g., 'Player/Mesh')","Component type names","Property names and values"],"output_types":["Hierarchical JSON representation of scene","Component property values","Modification confirmation/results"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ivanmurzak--unity-mcp__cap_3","uri":"capability://code.generation.editing.c.script.generation.and.in.editor.compilation.with.ai","name":"c# script generation and in-editor compilation with ai","description":"Enables AI clients to generate, modify, and compile C# scripts directly within the Unity Editor through MCP tools. The system handles script creation, code insertion/replacement, and triggers Unity's recompilation pipeline, providing real-time feedback on compilation errors. AI agents can write gameplay logic, editor tools, or utility scripts and immediately test them without manual file editing or IDE switching.","intents":["I want Claude to write a new C# script for me and have it immediately available in Unity","I need AI to modify existing scripts and see compilation errors in real-time","I want to generate boilerplate code (MonoBehaviors, ScriptableObjects, editor tools) through natural language"],"best_for":["Solo developers and small teams using AI as a coding assistant","Rapid prototyping workflows where iteration speed is critical","Developers learning C# who want AI-assisted code generation with immediate feedback"],"limitations":["Compilation errors block further AI operations — no graceful error recovery for syntax issues","No syntax validation before submission — invalid C# is sent to Unity compiler, causing delays","Script generation quality depends entirely on AI model capability — may produce non-idiomatic or inefficient code","No version control integration — AI-generated scripts are not tracked separately from manual edits"],"requires":["Unity 2022.3.61f1 or later","C# compiler available in Unity Editor","Write permissions to Assets folder"],"input_types":["C# code as text","Script file paths","Code modification instructions"],"output_types":["Generated C# script files","Compilation status (success/error)","Compiler error messages"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ivanmurzak--unity-mcp__cap_4","uri":"capability://automation.workflow.asset.import.and.metadata.manipulation.through.ai","name":"asset import and metadata manipulation through ai","description":"Exposes Unity's asset import pipeline and metadata system to AI clients, enabling them to configure import settings for textures, models, audio, and other assets without manual Inspector interaction. The system uses reflection to read/write asset importer properties and can trigger re-imports. AI agents can optimize asset settings (compression, format, LOD groups) based on project requirements or performance targets.","intents":["I want Claude to automatically configure texture compression and format for all my sprites","I need AI to adjust model import settings (rig, animation, LOD) based on my game's performance budget","I want to batch-configure audio import settings across multiple clips"],"best_for":["Game developers optimizing asset pipelines for performance","Teams automating asset configuration in CI/CD workflows","Developers managing large asset libraries who want AI-assisted bulk configuration"],"limitations":["Asset importer changes require re-import, which can be slow for large assets (texture re-compression may take seconds)","Only supports asset types with public importer APIs — custom asset types may not be accessible","Changes are not undoable through standard Undo system — requires manual reversion","No validation of import settings — invalid configurations may cause import failures"],"requires":["Unity 2022.3.61f1 or later","Asset files present in project","Write permissions to Assets and Library folders"],"input_types":["Asset paths","Importer property names and values","Asset type specifications"],"output_types":["Import configuration results","Re-import status","Asset metadata (size, format, compression)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ivanmurzak--unity-mcp__cap_5","uri":"capability://automation.workflow.unity.test.framework.integration.and.test.execution.via.ai","name":"unity test framework integration and test execution via ai","description":"Integrates with Unity's Test Framework (UTF) to enable AI clients to discover, execute, and analyze test results through MCP tools. The system can run unit tests, integration tests, and play mode tests, capture output and assertions, and report pass/fail status back to the AI client. This enables AI agents to validate code changes, detect regressions, and guide development toward passing tests.","intents":["I want Claude to run my test suite and tell me which tests are failing","I need AI to write tests for new features and verify they pass","I want to use test results to guide AI code generation — only accept changes that pass tests"],"best_for":["Test-driven development workflows with AI assistance","Teams using AI for code generation who need automated validation","Developers building AI-assisted game development pipelines with quality gates"],"limitations":["Play mode tests require Editor to be in play mode — cannot run in parallel with other operations","Test execution is synchronous and blocking — long test suites may timeout","No test coverage reporting — only pass/fail status is available","Flaky tests may cause AI to make incorrect decisions about code quality"],"requires":["Unity 2022.3.61f1 or later","Unity Test Framework package installed","Test assemblies compiled and available"],"input_types":["Test filter expressions","Test assembly names"],"output_types":["Test results (pass/fail/error)","Test output and assertion messages","Execution time metrics"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ivanmurzak--unity-mcp__cap_6","uri":"capability://tool.use.integration.multi.provider.llm.client.abstraction.with.unified.tool.calling","name":"multi-provider llm client abstraction with unified tool calling","description":"Abstracts differences between Claude, Gemini, OpenAI, and other LLM providers through a unified MCP client interface, handling provider-specific function-calling schemas, authentication, and response parsing. Developers configure their preferred LLM provider once, and all MCP tools work identically across providers without code changes. The system manages API keys, rate limiting, and provider-specific quirks transparently.","intents":["I want to switch from Claude to Gemini without changing my tool definitions","I need to use multiple LLM providers in the same project and have consistent tool calling","I want to abstract away provider-specific function-calling differences"],"best_for":["Teams evaluating multiple LLM providers","Developers building provider-agnostic AI integrations","Projects requiring fallback to alternative providers for cost or availability reasons"],"limitations":["Abstraction layer adds ~10-20ms overhead per tool call due to schema translation","Provider-specific features (vision, streaming, tool use variants) may not be fully exposed","Rate limiting and quota management must be configured per provider","Some providers have different tool calling semantics that cannot be fully abstracted"],"requires":["API keys for at least one LLM provider (Anthropic, OpenAI, Google)","Network connectivity to provider APIs","Configuration file specifying provider and credentials"],"input_types":["Tool definitions (MCP schema)","Provider configuration (API key, model name)"],"output_types":["Normalized tool call requests","Normalized tool results"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ivanmurzak--unity-mcp__cap_7","uri":"capability://automation.workflow.cli.based.project.setup.and.server.lifecycle.management","name":"cli-based project setup and server lifecycle management","description":"Provides a command-line interface for initializing Unity-MCP in a project, downloading platform-specific server binaries, configuring MCP clients (Claude Code, Cursor, Windsurf), and managing server startup/shutdown. The CLI automates the entire setup process, detecting the OS, downloading the correct binary, and generating configuration files for AI clients. Developers can get a fully functional AI-integrated Unity project running with a single command.","intents":["I want to set up Unity-MCP in my project with a single command","I need to download the correct server binary for my OS automatically","I want to configure Claude Code to work with my project without manual setup"],"best_for":["New projects starting with AI integration","Teams onboarding multiple developers who need consistent setup","Developers who want minimal configuration overhead"],"limitations":["CLI requires internet connectivity to download binaries — offline setup not supported","Binary downloads are large (~100MB+) — slow on limited bandwidth","Configuration is opinionated — advanced customization requires manual file editing","No rollback mechanism if setup fails partway through"],"requires":["Node.js 18+ or .NET CLI","Internet connectivity","Write permissions to project directory"],"input_types":["Project path","AI client type (Claude Code, Cursor, etc.)","Optional configuration flags"],"output_types":["Installed server binary","Configuration files","Setup status and instructions"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ivanmurzak--unity-mcp__cap_8","uri":"capability://automation.workflow.docker.containerized.deployment.with.multi.platform.support","name":"docker containerized deployment with multi-platform support","description":"Packages the MCP server as a Docker image (ivanmurzakdev/unity-mcp-server:latest) that can be deployed to cloud environments, CI/CD pipelines, or remote machines. The Docker image includes the ASP.NET Core runtime and all dependencies, enabling deployment without requiring .NET installation on the target system. Supports multi-platform builds (Linux x86_64, ARM64) for deployment to various cloud providers and edge devices.","intents":["I want to run Unity-MCP on a remote server for team collaboration","I need to integrate Unity-MCP into my CI/CD pipeline for automated testing","I want to deploy Unity-MCP to cloud infrastructure (AWS, GCP, Azure)"],"best_for":["Teams requiring centralized AI-assisted development infrastructure","CI/CD pipelines that need to run Unity tests with AI assistance","Organizations deploying AI services to cloud platforms"],"limitations":["Docker deployment requires running Unity Editor in headless mode — no visual feedback","Network latency between Docker container and AI client may exceed local deployment (50-200ms vs 20-50ms)","Container orchestration (Kubernetes) adds operational complexity","Licensing — Unity Editor requires appropriate license for headless/server deployment"],"requires":["Docker runtime installed","Docker image pulled from registry","Network connectivity between AI client and container","Appropriate Unity license for server deployment"],"input_types":["Docker image configuration","Environment variables (API keys, ports)"],"output_types":["Running MCP server container","Container logs and health status"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ivanmurzak--unity-mcp__cap_9","uri":"capability://memory.knowledge.real.time.resource.streaming.and.live.scene.updates","name":"real-time resource streaming and live scene updates","description":"Implements MCP resource subscriptions that stream real-time updates of scene state, GameObject hierarchy changes, and property modifications to AI clients. When the scene changes (GameObjects added/removed, properties modified), the system automatically pushes updates through the SignalR connection without requiring the AI client to poll. This enables AI agents to maintain an up-to-date mental model of the game state and react to changes in real-time.","intents":["I want Claude to be notified when I add or remove GameObjects from the scene","I need AI to track property changes and react to them automatically","I want real-time synchronization between the Unity Editor and AI client"],"best_for":["Interactive AI-assisted development workflows","Real-time collaborative editing scenarios","AI agents that need to maintain accurate scene state"],"limitations":["Streaming updates add network overhead — may impact performance on slow connections","Large scenes with frequent changes may overwhelm the AI client with update messages","No built-in filtering — all changes are streamed regardless of AI client interest","Update ordering is not guaranteed in high-frequency change scenarios"],"requires":["SignalR WebSocket connection active","MCP client supporting resource subscriptions"],"input_types":["Resource subscription requests"],"output_types":["Real-time resource update messages (JSON)","Scene state snapshots"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":52,"verified":false,"data_access_risk":"high","permissions":["Unity 2022.3.61f1 or later","C# 9.0+ for attribute syntax","Unity-MCP Plugin installed in project",".NET 6.0 or later runtime","ASP.NET Core 6.0+","Windows, macOS, or Linux OS","Open port for SignalR (default 5000)","Understanding of .NET reflection and serialization","Unity-MCP Plugin installed","MCP-compatible IDE (Claude Code, Cursor, Windsurf, VS Code 1.80+)"],"failure_modes":["Reflection-based approach adds ~50-100ms overhead per tool discovery at startup","Complex generic types or custom serialization may require manual schema overrides","Only supports methods with serializable parameter and return types (primitives, strings, objects with public properties)","Requires ASP.NET Core runtime (~100MB footprint) — not suitable for minimal embedded scenarios","SignalR WebSocket communication adds ~20-50ms latency per round-trip compared to direct in-process calls","Server binary must be running separately from Unity Editor — adds process management complexity","No built-in authentication — assumes trusted local network only","Attribute-based approach requires understanding of reflection and serialization","Complex type marshalling may require custom serializers","Async method support adds complexity to error handling and timeouts","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.5156802000802594,"quality":0.5,"ecosystem":0.8,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.550Z","last_scraped_at":"2026-05-03T13:56:59.049Z","last_commit":"2026-05-02T23:14:32Z"},"community":{"stars":2520,"forks":231,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=ivanmurzak--unity-mcp","compare_url":"https://unfragile.ai/compare?artifact=ivanmurzak--unity-mcp"}},"signature":"Zn5IrmdKrXkFuNDbcpn7Mf1RgMYw5mqR2n6eVYzMc8TeUcL+X5OysLgaiu9oNck0Aq/jGPR7UR/drJgjA8VMDg==","signedAt":"2026-06-20T15:14:55.506Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/ivanmurzak--unity-mcp","artifact":"https://unfragile.ai/ivanmurzak--unity-mcp","verify":"https://unfragile.ai/api/v1/verify?slug=ivanmurzak--unity-mcp","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}