https://github.com/qompassai/tko

Transforming Knowledge Ontology: The Model Context Protocol
https://github.com/qompassai/tko

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Transforming Knowledge Ontology: The Model Context Protocol

• Host: GitHub
• URL: https://github.com/qompassai/tko
• Owner: qompassai
• License: other
• Created: 2025-03-10T06:19:32.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2026-01-19T06:24:58.000Z (5 months ago)
• Last Synced: 2026-01-19T14:27:36.349Z (5 months ago)
• Language: JavaScript
• Size: 67.7 MB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Citation: CITATION.cff
  • Zenodo: .zenodo.json

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README

          


## Transforming Knowledge Ontology: The Model Context Protocol

Background

The convergence of generative artificial intelligence (genAI), quantum computing, and advanced cryptographic protocols

presents transformative opportunities in healthcare and education. As medical education evolves to incorporate AI-driven

tools, the integration of model context protocols (MCPs) with post-quantum cryptography (PQC) offers a promising

framework for secure, adaptive, and personalized learning environments. MCPs represent the progression of genAI towards

agentic behavior, with seamless integration between AI systems and external data sources by providing a universal

standard for connecting AI models to diverse tools and datasets. By establishing structured interactions between AI

models and external systems, MCP facilitates dynamic adaptation to specific contexts, such as medical education or

enterprise workflows. This protocol allows AI systems to preserve context across multiple tools, ensuring continuity and

relevance in their responses. At the heart of the MCP’s impact is its ability to foster agentic AI—autonomous systems

capable of executing tasks on behalf of users while maintaining context. This is achieved through two-way communication

between AI clients and MCP servers, where the servers provide specialized resources, tools, or prompts tailored to

specific tasks. For instance, in medical education, MCP could enable virtual patient simulations by connecting AI models

to clinical databases, curriculum repositories, and assessment tools. These simulations would allow students to practice

decision-making in controlled environments while dynamically adapting scenarios based on their performance. Another

critical aspect of MCP is its emphasis on scalability and interoperability. Developers can build modular AI applications

that adapt to new use cases without requiring extensive retraining or rewriting of application logic. This reusability

is further enhanced by pre-built MCP servers for popular platforms like Google Drive, Slack, GitHub, and Postgres. By

standardizing interactions through JSON-RPC workflows, MCP simplifies development processes and reduces maintenance

overhead. It essentially acts as the "USB-C" of AI integration—providing a universal connector for diverse systems.

* FDA/CISA warning about heart monitor

Methods

* PQC Algos

* MCP integration workflow diagram

* YT Demo/Gif

* LaTEX/MathJAX

* Code Snips

Results

* MCP Inspect

* RAG

* Redteaming

* Dioptra results

* HoneyPot Or BouncyCastle