| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 12 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Enables AI assistants to execute Unity Editor menu items (File, Edit, Assets, etc.) by translating natural language requests into JSON-RPC calls through a Node.js MCP server that relays commands via WebSocket to the Unity McpUnitySocketHandler, which dispatches them to the EditorApplication.ExecuteMenuItem API. This allows AI agents to trigger built-in editor workflows without direct UI interaction.
Unique: Uses MCP protocol as the transport layer for menu execution rather than direct REST/gRPC APIs, enabling seamless integration with AI assistants that natively support MCP (Claude, Windsurf) without custom client code. The WebSocket bridge pattern allows stateful editor context to persist across multiple AI requests.
vs alternatives: Simpler than building custom REST endpoints for each menu operation and more reliable than UI automation tools because it uses native EditorApplication APIs directly.
Provides AI assistants with read-only access to the complete scene hierarchy via MCP resources that serialize the Transform tree structure, enabling agents to query GameObject names, parent-child relationships, and active states. The McpUnitySocketHandler exposes scene data as JSON-RPC resources that can be filtered by name, tag, or layer, allowing AI to understand spatial relationships and select specific GameObjects for subsequent operations.
Unique: Exposes the entire scene hierarchy as a queryable MCP resource rather than requiring separate API calls per GameObject, enabling AI assistants to reason about spatial relationships and make informed decisions about which objects to target. Uses JSON serialization of Transform chains to preserve parent-child context.
vs alternatives: More efficient than querying individual GameObjects via separate API calls and provides richer context for AI reasoning compared to flat GameObject lists.
Provides Docker configuration and deployment scripts that containerize the Node.js MCP server, enabling AI-Unity integration to run in isolated environments without local Node.js installation. The Dockerfile packages the MCP server with dependencies and exposes the WebSocket port, allowing deployment to cloud environments or CI/CD pipelines with consistent runtime behavior.
Unique: Provides production-ready Docker configuration for the MCP server rather than requiring manual deployment setup, enabling teams to deploy AI-Unity integration to cloud environments without custom DevOps work. Includes environment variable configuration for flexible deployment scenarios.
vs alternatives: More portable than local Node.js installation and enables cloud deployment compared to desktop-only setups.
Implements a plugin-style architecture where new MCP tools and resources can be added by extending base handler classes and registering them with the tool/resource registry. The McpTools and McpResources base classes provide standard interfaces for tool execution and resource querying, allowing developers to add custom Unity operations without modifying core MCP server code.
Unique: Provides a clean handler interface that allows developers to add custom tools without modifying core MCP server code, following a plugin pattern. Uses TypeScript interfaces to enforce consistent handler signatures across custom implementations.
vs alternatives: More maintainable than monolithic tool implementations and enables community contributions compared to closed architectures.
Allows AI assistants to inspect all components attached to a selected GameObject and read their serialized properties (Transform position, Rigidbody mass, Collider bounds, etc.) through MCP resources that reflect the component hierarchy. The McpUnitySocketHandler serializes component data to JSON, exposing public fields, properties, and metadata that enable AI to understand the GameObject's behavior and make informed modification decisions.
Unique: Uses Unity's serialization system to expose component properties as queryable JSON rather than requiring AI to parse binary asset files or use reflection directly, making component state transparent to AI agents without deep Unity knowledge. Integrates with the MCP resource registry to provide consistent access patterns.
vs alternatives: More reliable than parsing .meta files or asset bundles and provides real-time component state compared to static asset analysis.
Enables AI assistants to create new GameObjects and attach components with specified properties by translating natural language requests into JSON-RPC tool calls that invoke Unity's Instantiate and AddComponent APIs. The McpUnitySocketHandler processes tool requests to create GameObjects with initial Transform values, add components like Rigidbody or Collider, and set their properties in a single atomic operation, allowing AI to build scene content programmatically.
Unique: Combines GameObject instantiation and component addition into a single MCP tool call with property initialization, reducing round-trip latency compared to separate create/configure operations. Uses JSON schema validation to ensure property types match component expectations before execution.
vs alternatives: Faster than sequential API calls and more reliable than script-based creation because it uses native Unity APIs with immediate validation feedback.
Provides AI assistants with access to Unity Editor console output through MCP resources that stream or snapshot debug logs, warnings, and errors with timestamps and stack traces. The getConsoleLogResource handler captures logs from Unity's Debug.Log system and exposes them as queryable JSON, allowing AI to monitor build errors, runtime warnings, and script execution feedback without parsing console UI.
Unique: Exposes Unity's internal Debug.Log stream as a queryable MCP resource rather than requiring AI to parse console UI text, enabling structured error analysis and automated error detection. Integrates with the resource registry to provide consistent polling/subscription patterns.
vs alternatives: More reliable than screen scraping console UI and provides structured data that AI can parse programmatically compared to unstructured log text.
Enables AI assistants to search the Unity asset database for prefabs, scripts, scenes, and other assets by name or type through MCP resources that query the AssetDatabase API. The McpUnitySocketHandler exposes asset metadata (path, type, GUID) as JSON, allowing AI to discover available resources before referencing them in creation or modification operations.
Unique: Wraps Unity's AssetDatabase API as MCP tools/resources, providing AI with structured asset discovery without requiring direct API knowledge. Uses GUID-based asset references to ensure stability across asset moves.
vs alternatives: More reliable than file system scanning because it uses Unity's internal asset database and respects import settings and asset metadata.
+4 more capabilities
Executes SQL queries against Supabase PostgreSQL instances through the Model Context Protocol, translating natural language or structured query requests into parameterized SQL statements. Uses MCP's tool-calling interface to expose database operations as callable functions with schema validation, enabling LLM agents to perform CRUD operations, joins, and aggregations with automatic connection pooling and credential management through Supabase client SDK.
Unique: Exposes Supabase PostgreSQL as MCP tools with automatic credential injection from Supabase client SDK, eliminating manual connection string management and enabling seamless LLM-to-database queries within Claude or compatible agents
vs alternatives: Tighter integration than generic SQL MCP servers because it leverages Supabase's built-in authentication and connection pooling rather than requiring separate database credential configuration
Exposes Supabase Auth session state and user metadata through MCP tools, allowing agents to inspect current authentication context, retrieve user profiles, and trigger auth-related operations. Integrates with Supabase's JWT-based auth system to validate sessions and access user claims without re-authenticating, using the Supabase client's built-in session management.
Unique: Integrates Supabase's JWT-based auth system directly into MCP tool interface, allowing agents to inspect and act on auth state without managing separate credential stores or re-authentication flows
vs alternatives: More seamless than generic auth MCP servers because it leverages Supabase's built-in session management and avoids redundant credential passing between agent and auth system
Invokes Supabase Edge Functions (serverless TypeScript/JavaScript functions) through MCP tools, passing parameters and receiving results with optional streaming support. Uses Supabase's edge function HTTP API to trigger functions with automatic authentication headers and response parsing, enabling agents to execute custom business logic without embedding it in the agent itself.
Supabase scores higher at 42/100 vs Unity3d Game Engine at 23/100.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
Unique: Exposes Supabase Edge Functions as MCP tools with automatic authentication and response parsing, allowing agents to invoke custom serverless logic without managing HTTP clients or credential injection
vs alternatives: More integrated than generic HTTP MCP tools because it handles Supabase-specific authentication, error handling, and response formatting automatically
Subscribes to real-time changes on Supabase tables through MCP's event streaming interface, using Supabase's PostgreSQL LISTEN/NOTIFY mechanism to push INSERT, UPDATE, and DELETE events to agents. Maintains persistent WebSocket connections and filters events by table and row-level policies, enabling agents to react to database changes without polling.
Unique: Bridges Supabase's PostgreSQL LISTEN/NOTIFY real-time system with MCP's tool interface, enabling agents to subscribe to database changes without managing WebSocket connections or event serialization
vs alternatives: More efficient than polling-based approaches because it uses Supabase's native real-time infrastructure rather than repeated database queries
Manages files in Supabase Storage buckets through MCP tools, supporting upload, download, list, and delete operations with automatic authentication and path-based access control. Uses Supabase's S3-compatible storage API with built-in support for public/private buckets and signed URLs for temporary access, enabling agents to handle file I/O without managing cloud storage credentials.
Unique: Exposes Supabase Storage's S3-compatible API as MCP tools with automatic authentication and signed URL generation, eliminating the need for agents to manage cloud storage credentials or generate temporary access tokens
vs alternatives: More integrated than generic S3 MCP tools because it leverages Supabase's built-in bucket policies and authentication rather than requiring separate AWS credentials
Performs semantic similarity searches on vector embeddings stored in Supabase PostgreSQL using pgvector extension, translating natural language queries into embedding vectors and executing cosine/L2 distance searches. Integrates with embedding providers (OpenAI, Cohere) or uses pre-computed embeddings, enabling agents to retrieve semantically similar documents or records without full-text search limitations.
Unique: Integrates pgvector directly into MCP tools with automatic embedding generation and distance calculation, enabling agents to perform semantic search without managing separate vector database infrastructure
vs alternatives: More efficient than external vector databases (Pinecone, Weaviate) for Supabase users because it colocates embeddings with relational data, reducing network latency and simplifying data synchronization
Exposes Supabase database schema information through MCP tools, allowing agents to discover table structures, column types, constraints, and relationships without manual schema documentation. Queries PostgreSQL information_schema and Supabase metadata tables to dynamically generate schema descriptions, enabling agents to construct valid queries and understand data relationships.
Unique: Queries Supabase's PostgreSQL information_schema directly through MCP tools, enabling agents to dynamically discover and adapt to database schemas without pre-configured schema definitions
vs alternatives: More flexible than static schema definitions because it reflects live database state, including recent migrations or schema changes
Enforces Supabase Row-Level Security policies within agent queries, ensuring that agents can only access rows permitted by RLS rules defined in the database. Evaluates policies based on authenticated user context (JWT claims, user ID) and applies WHERE clause filters automatically, preventing unauthorized data access at the database layer rather than application layer.
Unique: Delegates authorization enforcement to PostgreSQL RLS policies rather than implementing authorization in agent code, ensuring that data access rules are centralized and cannot be bypassed by agent logic
vs alternatives: More secure than application-level authorization because RLS is enforced at the database layer, preventing accidental data leaks even if agent code has bugs
+1 more capabilities