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MCP-Blockly

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owenkaplinsky commited on 22 days ago

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@@ -66,8 +66,6 @@ When you arrange blocks in the editor, change listeners trigger code generation.
 Blocks dynamically manage their input and output ports through Blockly's mutator system. When you modify a block to add or remove parameters, the mutator updates both the visual shape and the internal state that tracks how many inputs and outputs exist. Each input and output has metadata about its name and type. When a user defines inputs on their main function block, the system creates invisible reference blocks for each input parameter. These reference blocks appear as connectable outputs that other blocks can use. During code generation, these references translate to variable names in the Python function signature and body.
-Code execution happens in a sandboxed Python environment. User code is executed with restricted builtins and a clean state for each run. The system captures return values and displays them in the test interface.
 The AI Assistant component is the sophisticated heart of the system. It continuously monitors the current workspace state and code. When you send a message, the system formats your entire block structure into a readable representation and includes it in the context sent to OpenAI. The model receives not just your question but a complete understanding of what you've built. The system includes a detailed system prompt that explains MCP concepts, the block syntax, and what actions the model can perform.
 Based on the model's response, the system recognizes four special commands: run to execute your MCP with sample inputs, delete to remove a block by ID, create to add new blocks to your workspace, and deploy_to_huggingface to publish your tool as a live server. When the model issues these commands, they're executed immediately. For block modifications, the system uses Server-Sent Events to stream commands back to the frontend, which creates or deletes blocks in real time while you watch. This maintains real-time synchronization between the chat interface and the visual editor.

 Blocks dynamically manage their input and output ports through Blockly's mutator system. When you modify a block to add or remove parameters, the mutator updates both the visual shape and the internal state that tracks how many inputs and outputs exist. Each input and output has metadata about its name and type. When a user defines inputs on their main function block, the system creates invisible reference blocks for each input parameter. These reference blocks appear as connectable outputs that other blocks can use. During code generation, these references translate to variable names in the Python function signature and body.
 The AI Assistant component is the sophisticated heart of the system. It continuously monitors the current workspace state and code. When you send a message, the system formats your entire block structure into a readable representation and includes it in the context sent to OpenAI. The model receives not just your question but a complete understanding of what you've built. The system includes a detailed system prompt that explains MCP concepts, the block syntax, and what actions the model can perform.
 Based on the model's response, the system recognizes four special commands: run to execute your MCP with sample inputs, delete to remove a block by ID, create to add new blocks to your workspace, and deploy_to_huggingface to publish your tool as a live server. When the model issues these commands, they're executed immediately. For block modifications, the system uses Server-Sent Events to stream commands back to the frontend, which creates or deletes blocks in real time while you watch. This maintains real-time synchronization between the chat interface and the visual editor.