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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 9 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Executes arbitrary MEL (Maya Embedded Language) and Python commands directly within Autodesk Maya through the Model Context Protocol, translating MCP tool calls into Maya's command queue with real-time execution and result streaming back to the client. Implements bidirectional communication between Claude/LLM clients and Maya's scripting engine, enabling remote automation without manual script file creation or Maya UI interaction.
Unique: Bridges Claude/LLM agents directly to Maya's scripting engine via MCP protocol, enabling stateful command sequences where each command can reference previous results — unlike REST API wrappers that require explicit state management between calls. Implements Maya-specific tool schemas that expose both MEL and Python execution paths with automatic result serialization.
vs alternatives: Tighter integration than generic Python subprocess wrappers because it uses MCP's native tool-calling semantics, allowing Claude to reason about available Maya operations as first-class tools rather than generic script execution.
Provides structured read-only access to Maya scene hierarchy, object properties, transform data, and material assignments through MCP tools that parse Maya's scene graph and return JSON-serialized results. Implements lazy-loaded scene introspection where queries are executed on-demand rather than caching the entire scene, reducing memory overhead and ensuring real-time accuracy when the scene is modified externally.
Unique: Exposes Maya's scene graph as queryable JSON structures through MCP, allowing LLMs to reason about 3D scene composition without requiring knowledge of MEL/Python syntax. Implements on-demand scene traversal rather than full caching, enabling real-time accuracy in dynamic workflows.
vs alternatives: More accessible than raw MEL/Python queries because it abstracts scene graph complexity into structured JSON, allowing non-technical users or LLMs to understand scene state without learning Maya scripting.
Supports creating multiple objects (meshes, cameras, lights, deformers) and modifying their properties in a single MCP call through batched command execution. Translates high-level creation requests (e.g., 'create 5 cubes in a grid') into optimized MEL/Python sequences that minimize round-trip latency and maintain referential integrity across created objects.
Unique: Batches multiple object creation and modification commands into optimized MEL/Python sequences executed in a single Maya command, reducing network round-trips and improving performance compared to individual command execution. Maintains referential integrity across created objects within a batch.
vs alternatives: More efficient than sequential individual commands because it groups operations into a single Maya transaction, reducing latency overhead and enabling atomic rollback if any operation fails.
Executes arbitrary MEL and Python code snippets within Maya's runtime environment, streaming execution results and error messages back to the MCP client in real-time. Implements a dual-path execution model where Python is preferred for modern workflows but MEL is supported for legacy scripts, with automatic syntax detection and error context preservation.
Unique: Provides direct code execution access to Maya's scripting engine with dual MEL/Python support and real-time result streaming, enabling LLMs to generate and execute complex procedural logic without intermediate file I/O. Implements automatic syntax detection to route code to the appropriate interpreter.
vs alternatives: More flexible than tool-based execution because it allows arbitrary code generation, but requires careful prompt engineering to ensure LLMs generate syntactically valid MEL/Python code.
Manages Maya's selection state and execution context through MCP tools that can set/clear selections, query current selection, and maintain context across multiple command executions. Implements a stateful selection model where selections persist between commands, enabling LLM agents to build up complex selections through multiple operations (e.g., 'select all red objects, then add all lights to selection').
Unique: Exposes Maya's selection state as a stateful MCP resource that persists across multiple tool calls, allowing LLM agents to build complex selections iteratively without re-specifying object lists. Implements selection mode semantics (replace, add, remove) familiar to Maya users.
vs alternatives: More intuitive for Maya users than explicit object lists because it leverages Maya's native selection model, but requires careful coordination when multiple clients access the same Maya instance.
Provides MCP tools for reading and writing object transforms (position, rotation, scale) and arbitrary attributes with support for animated values, constraints, and expressions. Implements attribute-level access to Maya's dependency graph, enabling precise control over object properties and animation without requiring knowledge of MEL/Python syntax.
Unique: Exposes Maya's dependency graph attribute system through high-level MCP tools that abstract away MEL/Python syntax, enabling LLMs to manipulate transforms and custom attributes without scripting knowledge. Supports both static values and animated keyframes in a unified interface.
vs alternatives: More accessible than raw MEL/Python because it provides semantic tools for common operations (set position, add keyframe, apply constraint) rather than requiring users to understand Maya's attribute syntax.
Manages material and shader assignments through MCP tools that can create materials, assign them to objects, and query material properties. Implements a simplified material workflow that abstracts Maya's complex shader graph into high-level operations (assign material, set color, set texture) suitable for LLM-driven workflows.
Unique: Provides high-level material assignment tools that abstract Maya's complex shader graph into semantic operations (assign material, set color, set texture), enabling LLMs to manage materials without understanding shader networks. Implements a simplified material model suitable for procedural workflows.
vs alternatives: More user-friendly than direct shader graph manipulation because it exposes common material operations as simple tools, but less flexible for complex shader networks that require direct graph access.
Provides MCP tools for creating and configuring deformers (blend shapes, skin clusters, joints) and building simple rigs through high-level operations. Implements a deformer abstraction layer that translates semantic requests (e.g., 'create blend shape for facial animation') into appropriate MEL/Python commands with automatic setup and configuration.
Unique: Abstracts Maya's complex deformer and rigging systems into semantic MCP tools that enable LLMs to create and configure deformers without understanding MEL/Python rigging syntax. Implements automatic setup and configuration for common deformer types.
vs alternatives: More accessible than raw MEL/Python rigging because it provides high-level deformer operations, but less flexible for complex rigs that require manual weight painting and constraint setup.
+1 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 Maya MCP at 22/100.
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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