The MCP Craze: New Servers Fuel the Hype
From Simple MPC Servers to MPC Everything: The Rise of World Model Context Protocol
BlenderMCP — Blender Model Context Protocol Integration
Summary: Creating 3D Models with Cloud AI & Blender Using MCP
Introduction
- The tutorial demonstrates how to use Claude AI with Blender via MCP (a tool connecting AI to applications).
- The AI generates Python code to create 3D objects, materials, and lighting in Blender.
- The process helps beginners learn Blender by providing a foundational understanding of its UI, tools, and scripting.
Key Steps
Setup & Installation
- Download Blender (free and open-source).
- Install Node.js, Python, and Homebrew (for terminal commands).
- Download the Blender MCP add-on from GitHub and install it in Blender.
- Configure Claude AI’s desktop app to connect with MCP by editing the
config.jsfile.
Connecting Claude AI to Blender
- Start Blender, enable the MCP add-on, and launch the server.
- Open Claude AI, ensure the MCP tool for Blender is active, and request 3D model generation (e.g., “Create a 3D house”).
- The AI generates Python code, which Blender executes to build objects, textures, and lighting.
Results & Experimentation
- Basic models (houses, icons, Roblox-style characters) are created, though positioning/quality may vary.
- AI can animate objects (e.g., simple movements) and export scenes to Three.js for web projects.
- Users can refine models by prompting adjustments (e.g., “Add a floor and better lighting”).
Advanced Features
- File System Integration: Claude can write files (e.g., SwiftUI tutorials) to the local system.
- Patterns & Custom Shapes: AI generates geometric patterns or replicates designs from reference images (e.g., Midjourney concepts).
- Troubleshooting: Ensure correct startup order (Claude → Blender → MCP server) and check Python/UV installations.
Key Takeaways
- MCP bridges AI and applications, enabling automation in 3D modeling, file management, and more.
- Beginners learn Blender’s basics (navigation, keyframing, materials) through AI-assisted experimentation.
- Future potential: Iterative improvements, integration with other tools (Figma, databases), and expanded creative workflows.
Final Thought: This method democratizes 3D design, allowing users to prototype quickly while learning Blender’s fundamentals. Experimentation and iterative prompting yield the best results.
Example AI Outputs: Basic house models, animated characters, geometric patterns, and exported Three.js projects.
Tools Used: Blender, Claude AI, MCP, Python, Node.js, Homebrew.
For More: Refer to the GitHub repo for Blender MCP and Claude’s MCP documentation.
The Rise of MCP and DeMCP in the AI Revolution
Key Points:
MCP (Model Context Protocol) Gains Traction
- Developed by Anthropic in 2024, MCP is a universal protocol for AI integration, likened to “USB-C for AI.”
- OpenAI’s CEO Sam Altman announced full support for MCP, integrating it into OpenAI’s Agents SDK and soon ChatGPT.
- Major companies like Microsoft, Cursor, and Apollo have also adopted MCP, signaling its potential as an AI standard.
MCP’s Functionality & Challenges
- Enables AI models to interact seamlessly with external tools (e.g., Google Drive, Slack, GitHub).
- Current limitations:
- Reliance on local stdio mode restricts scalability.
- Security and privacy risks due to lack of oversight.
- Unclear revenue-sharing models hinder sustainable development.
DeMCP: A Decentralized Solution
- First decentralized MCP ecosystem, leveraging blockchain and Trusted Execution Environment (TEE) for security.
Features:
- Secure remote access to MCP services without local installation.
- Token incentives & revenue-sharing to reward developers.
- LLM subsidy program to lower costs for global developers.
Current Integrations & Web3 Focus
- Supports 50+ Web3 MCP services (e.g., on-chain data queries, token trading via Uniswap, social media management).
- Integrates 10+ LLM APIs, including GPT-4o, Gemini-1.5, and Claude 3.7.
- Aims to be the MCP hub for Web3, enabling easy development of AI agents for decentralized applications.
Future Outlook
MCP is becoming a major AI trend, with DeMCP positioning itself as the go-to infrastructure for secure, scalable, and incentivized AI-agent development in Web3.
Conclusion:
DeMCP addresses MCP’s current limitations by providing a decentralized, secure, and economically sustainable ecosystem, paving the way for the next generation of AI agents in both Web2 and Web3 environments.
What is the Model Context Protocol (MCP) and how does it work with AWS MCP Servers?
The Model Context Protocol (MCP) is an open protocol that enables seamless integration between LLM applications and external data sources and tools. Whether you’re building an AI-powered IDE, enhancing a chat interface, or creating custom AI workflows, MCP provides a standardized way to connect LLMs with the context they need.
An MCP Server is a lightweight program that exposes specific capabilities through the standardized Model Context Protocol. Host applications (such as chatbots, IDEs, and other AI tools) have MCP clients that maintain 1:1 connections with MCP servers. Common MCP clients includ agentic AI coding assistants (like Q Developer, Cline, Cursor, Windsurf) as well as chatbot applications like Claude Desktop, with more clients coming soon. MCP servers can access local data sources and remote services to provide additional context that improves the generated outputs from the models.
AWS MCP Servers use this protocol to provide AI applications access to AWS documentation, contextual guidance, and best practices. Through the standardized MCP client-server architecture, AWS capabilities become an intelligent extension of your development environment or AI application.
AWS MCP servers enable enhanced cloud-native development, infrastructure management, and development workflows — making AI-assisted cloud computing more accessible and efficient.
The Model Context Protocol is an open source project run by Anthropic, PBC. and open to contributions from the entire community. For more information on MCP, you can find further documentation here
Why MCP Servers?
MCP servers enhance the capabilities of foundation models (FMs) in several key ways:
Improved Output Quality: By providing relevant information directly in the model’s context, MCP servers significantly improve model responses for specialized domains like AWS services. This approach reduces hallucinations, provides more accurate technical details, enables more precise code generation, and ensures recommendations align with current AWS best practices and service capabilities.
Access to Latest Documentation: FMs may not have knowledge of recent releases, APIs, or SDKs. MCP servers bridge this gap by pulling in up-to-date documentation, ensuring your AI assistant always works with the latest AWS capabilities.
Workflow Automation: MCP servers convert common workflows into tools that foundation models can use directly. Whether it’s CDK, Terraform, or other AWS-specific workflows, these tools enable AI assistants to perform complex tasks with greater accuracy and efficiency.
Specialized Domain Knowledge: MCP servers provide deep, contextual knowledge about AWS services that might not be fully represented in foundation models’ training data, enabling more accurate and helpful responses for cloud development tasks.
