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PeterPanino
   
989 Posts |
Posted - Jul 13 2025 : 07:40:47
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CONCEPT: MCP (Model Context Protocol) servers are a standardized way for AI assistants like Claude to connect to external tools and data sources. Think of them as specialized middleware that acts as a bridge between AI models and various applications, databases, or services.
There are several excellent websites that explain MCP servers in detail. The main resources are: Official Documentation:
https://modelcontextprotocol.io/ - This is the primary official website for the Model Context Protocol. MCP is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications. Model Context Protocol: Introduction
Key Resources:
Anthropic's announcement: https://www.anthropic.com/news/model-context-protocol - The Model Context Protocol is an open standard that enables developers to build secure, two-way connections between their data sources and AI-powered tools. Introducing the Model Context Protocol \ Anthropic GitHub Repository: https://github.com/modelcontextprotocol - The Model Context Protocol (MCP) is an open protocol that enables seamless integration between LLM applications and external data sources and tools. Model Context Protocol · GitHub Server Examples: https://github.com/modelcontextprotocol/servers - This repository contains many example MCP servers you can study or use as starting points.
Additional Learning Resources:
Cursor Documentation: https://docs.cursor.com/context/model-context-protocol - Shows how MCP is implemented in the Cursor IDE Medium Tutorial: There's a comprehensive tutorial on Medium by Dr. Nimrita Koul that walks through MCP implementation
The official website (modelcontextprotocol.io) is your best starting point - it includes the full specification, getting started guides, and examples. The GitHub repository has practical server implementations you can examine to understand how to build your own MCP server for the ImageEn library use case you mentioned.
QUESTION: Is it possible to build an MCP server that accesses a set of source-code (documentation) files and uses the knowledge included in these files to solve problems?
ANSWER: Yes, absolutely! Building an MCP server that accesses source code (documentation) files and uses that knowledge to solve problems is a very practical and powerful use case. Your ImageEn library example is perfect for this.
The MCP server could expose resources like:
• Library documentation and API references • Example code snippets organized by functionality • Class hierarchies and method signatures • Common usage patterns and best practices
When you ask "build a specific ImageEn demo app," the AI assistant would:
Query the MCP server for relevant ImageEn components Retrieve example code showing similar functionality Access API documentation for the specific features needed Use this context to generate a complete, working demo application (or even a complete user application ready for production!)
Technical benefits: This approach is much more effective than trying to rely on the AI's training data alone, because:
• It accesses the most current version of the library • It can reference actual working examples from the codebase • It understands the specific conventions and patterns used in that library • It can provide more accurate and up-to-date API usage
Example workflow: You: "Create an ImageEn demo that loads an image, applies a blur filter, and saves the result" MCP Server: Returns relevant classes, methods, and example code AI Assistant: Generates a complete demo using the actual ImageEn API
This would be incredibly useful for any substantial code library or framework where you want AI assistance that's grounded in the actual source code and examples.
What do you think? |
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xequte
    
39097 Posts |
Posted - Jul 13 2025 : 22:58:32
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Hi
Yes, that would be good. Probably not something we'll have time to implement in the near future.
Nigel Xequte Software www.imageen.com
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PeterPanino
   
989 Posts |
Posted - Jul 15 2025 : 05:45:43
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MCP is the next big thing and the future of AI and software libraries. |
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