| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 12 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Implements a three-tier architecture that translates Model Context Protocol (MCP) tool calls from AI clients into XML-RPC method invocations against a FreeCAD addon server running on localhost:9875. The FastMCP framework exposes FreeCAD operations as standardized MCP tools, while a singleton FreeCADConnection class manages the XML-RPC client connection with automatic reconnection and error handling. This decouples AI frameworks (Claude Desktop, LangChain, Google ADK) from FreeCAD's GUI thread, enabling safe asynchronous control.
Unique: Uses FastMCP framework with a dedicated FreeCADConnection singleton to abstract XML-RPC complexity, enabling multiple AI frameworks to control FreeCAD through standardized MCP protocol without modifying FreeCAD's core codebase — the addon runs as a separate workbench with thread-safe QTimer-based task queuing
vs alternatives: Unlike direct FreeCAD Python API calls or REST wrappers, this approach maintains FreeCAD's GUI responsiveness by queuing operations through the Qt event loop while supporting multiple concurrent AI clients via MCP's standardized interface
Exposes a create_object MCP tool that instantiates FreeCAD objects across multiple workbenches (Part, PartDesign, Draft, Sketcher, Assembly, etc.) by accepting a type string and property dictionary. The RPC server's object creation logic maps type names to FreeCAD class constructors, sets properties via setattr, and returns serialized object metadata including UUID, label, and computed properties. Supports complex objects like PartDesign::Body with nested features and Draft objects with geometric constraints.
Unique: Abstracts FreeCAD's multi-workbench object model through a unified create_object interface that handles type-specific initialization, property serialization, and computed property calculation — enabling AI agents to reason about CAD objects without deep FreeCAD API knowledge
vs alternatives: More flexible than FreeCAD's native Python API for AI use because it returns serialized object state immediately and handles workbench-specific initialization transparently, whereas direct API calls require knowledge of each workbench's object hierarchy
Implements a FreeCADConnection singleton class that manages the XML-RPC client connection to the FreeCAD addon server. The singleton maintains a persistent connection, automatically reconnects on failure with exponential backoff, and provides a unified interface for all RPC calls. Connection state is cached to avoid repeated connection attempts. The MCP server instantiates this singleton once and reuses it for all tool invocations, ensuring connection pooling and efficient resource usage.
Unique: Uses a singleton pattern with automatic reconnection logic to abstract away XML-RPC connection complexity, allowing MCP tools to invoke FreeCAD operations without managing connection state — the connection is transparent to tool implementations
vs alternatives: More resilient than naive RPC clients because it implements exponential backoff and automatic reconnection; more efficient than creating new connections per request because it reuses a single persistent connection
Implements object serialization logic in the RPC server that converts FreeCAD objects to JSON-compatible dictionaries. The serializer traverses object attributes, computes derived properties (e.g., bounding box, volume, mass), handles special types (lists, nested objects, geometry data), and encodes them as JSON. Computed properties are calculated on-demand and cached per object. The serializer handles type coercion for non-JSON types (e.g., converting vectors to tuples, colors to hex strings). Enables AI agents to reason about object state without understanding FreeCAD's internal object model.
Unique: Automatically calculates and includes computed properties (volume, mass, bounding box) in serialized objects, providing AI agents with derived metrics without requiring separate analysis steps — the RPC server handles all geometry calculations transparently
vs alternatives: More informative than raw property dumps because it includes computed metrics; more efficient than requiring separate analysis calls because properties are calculated once during serialization
Implements a get_view MCP tool that captures PNG screenshots of the FreeCAD 3D viewport from specified viewpoints (Isometric, Front, Top, Bottom, Left, Right, etc.) by invoking FreeCAD's camera positioning API and rendering the scene. Screenshots are base64-encoded and returned in the MCP response, enabling AI agents to receive visual feedback on model state without opening the FreeCAD GUI. The RPC server handles viewport rendering synchronously within the Qt event loop.
Unique: Bridges FreeCAD's native viewport rendering with MCP's JSON protocol by capturing and base64-encoding screenshots, allowing vision-capable AI models to inspect CAD geometry without requiring separate image file I/O or display server access
vs alternatives: Unlike file-based screenshot approaches, this returns images directly in MCP responses, enabling stateless AI workflows without filesystem dependencies; unlike headless rendering, it leverages FreeCAD's native GPU-accelerated viewport
Exposes an execute_code MCP tool that accepts arbitrary Python code strings and executes them within FreeCAD's Python interpreter, with access to the FreeCAD API (App, Gui modules) and the current document. Code execution happens synchronously in the RPC server's thread, with stdout/stderr captured and returned in the response. This enables AI agents to perform complex operations not exposed by dedicated MCP tools, such as custom geometry calculations, macro-like workflows, or debugging.
Unique: Provides direct Python code execution within FreeCAD's runtime via MCP, allowing AI agents to leverage FreeCAD's full Python API without being constrained to predefined tool schemas — trades safety for flexibility and expressiveness
vs alternatives: More powerful than tool-based approaches because it enables one-shot execution of complex workflows, but less safe than sandboxed execution environments; positioned for trusted, internal AI automation rather than public-facing services
Implements get_objects and get_object MCP tools that query FreeCAD document structure and return serialized object metadata including properties, computed values, and hierarchical relationships. The RPC server traverses the document's object tree, serializes each object's attributes to JSON, and handles special cases like sketches with geometry data and assemblies with part references. Enables AI agents to understand current CAD state without visual inspection.
Unique: Serializes FreeCAD's internal object graph to JSON with computed properties included, enabling AI agents to reason about CAD state without parsing binary FreeCAD files or maintaining separate state tracking — the RPC server handles all serialization complexity
vs alternatives: More accessible than direct FreeCAD Python API introspection because it returns structured JSON; more complete than file-based approaches because it includes computed/derived properties and real-time state
Exposes an edit_object MCP tool that modifies properties of existing FreeCAD objects by accepting an object ID and property dictionary, then using Python's setattr to apply changes. The RPC server validates property types against the object's class definition and returns updated object metadata. Supports both simple properties (dimensions, colors) and complex properties (lists, nested objects). Changes are immediately reflected in the FreeCAD document.
Unique: Provides direct property mutation through MCP without requiring knowledge of FreeCAD's property editor UI or Python API details — the RPC server handles type coercion and attribute setting transparently
vs alternatives: Simpler than FreeCAD's native Python API for AI use because it accepts flat JSON property dictionaries; more flexible than GUI-based editing because it enables programmatic batch updates
+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 freecad-mcp at 30/100.
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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