Exercise: Integrate Model Context Protocol with GitHub Copilot

Exercise: Integrate Model Context Protocol with GitHub Copilot

Exercise: Integrate Model Context Protocol with GitHub Copilot

Exercise: Integrate Model Context Protocol with GitHub Copilot

Exercise: Integrate Model Context Protocol with GitHub Copilot

Demonstrates autonomous querying of court processes via a Model Context Protocol workflow with TJSP data, highlighting cross-tool reasoning and safe data orchestration.

VSCode Model Context Protocol configuration and setup

Proof-of-concept tool enabling AI-assisted review by fetching GitLab MR info and comments.

Curated templates to standardize Model Context Protocol implementations, speeding scaffolding, schemas, and cross-project compatibility.

Lightweight MuleSoft MCP test harness for local model-context simulation and validation.

A modular, Dockerized system coordinating specialized agents via A2A protocol and tool-enabled integration for drafting, research, and cloud publication.

Seamless Claude AI integration with desktop apps using MCP for real-time, context-aware automation and control.

A lightweight, runnable Todo workflow demo showing chat-API coordination and port-separated services for clear, modular development.

Go-based reference implementation standardizing context delivery from apps to LLMs with modular data/tool adapters.

Comprehensive template enabling Claude-4-Sonnet integration, enterprise-grade security, automated setup, and extensible architecture across macOS, Linux, and Windows to streamline multi-domain AI deployments.

MCP_Model_Context_Protocol

Lightweight, async Python server for dynamic model context management and efficient orchestration.

Demonstrates practical integration patterns for context-aware tool orchestration and autonomous agent workflows.

Modular, context-aware agents coordinating across tools for adaptive, auditable workflows with interoperability and secure execution.

Unified, interoperable context management enabling dynamic, secure sharing and orchestration across AI components.

Client exposed ecosystem that shares tools and data with LLM apps using flexible transports and prompt templates.

A scalable MCP server enabling structured model-context exchange and context-aware routing across AI workloads.

MCP Client and Servers Implementation

Standardized interface linking LLMs to diverse data sources and tools via a secure, observable gateway.