Two-context demo with journey and news domains, configurable OpenAI backend via env vars, using ports 8000 and 8001.

Synchronous weather tool with dual transport (stdio & HTTP), enabling tool discovery, forecast and alerts access via JSON-RPC.

Automatic parsing and Markdown documentation with parameter details, code quality insights, and comment preservation.

A Python-based demonstration of context handling with clear tooling interactions and extensible patterns.

Lightweight Python-based orchestration with memory and tools for coherent interactions across tasks

Demonstrates a stdio-driven MCP using Tavily web search for rapid Cursor workflows.

Model Context Protocol (MCP) is an API interface for your applications that are formatted in a way that machine learning platforms can interact with them. They can be used to generate AI insights, perform tasks based on user input or other things.

Python-first hub enabling modular adapters and standardized context sharing across projects.

A modular host-client-server framework coordinating AI resources through JSON-RPC, enabling specialized, external service integration and dynamic resource orchestration.

OpenAI-based MCP with real data access and file/web tool integrations.

Minimal toolkit showcasing core tools and resources with quick local setup, CLI workflow, and VS Code integration for rapid AI tool prototyping.

A lightweight protocol for centralized model context sharing and collaboration across heterogeneous AI agents, emphasizing efficient context transfer and modular plug-ins.

Model Context Protocol

A minimal LangChain MCP setup that links math and weather tools into a single adaptable agent with HTTP servers.

Model Context Protocol

Model Context Protocol

Streamlit-based MCP tool that crafts personalized learning paths by integrating YouTube, Drive, and Notion for cohesive, goal-driven study plans.

Standardized context exchange enabling cross-model collaboration, adaptive routing, and tool integration through pluggable adapters and policies.

CLI AI chat with document-aware prompts and extensible tool integrations, powered by an Anthropic API-backed workflow for knowledge-driven conversations.

Context-driven weather insights with adaptable data sources and auditable decision traces, enabling proactive alerts and cross-actor collaboration.

Model_context_protocol

TypeScript-based backend with real-time resume-driven interactions, multi-format resume support, and SMTP email alerts, designed for modular, extensible model-context tasks.

Async Python toolset for local file manipulation, command execution, and quick MCP experiments via a terminal client.

End-to-end deployment with AI Foundry integration and Azure APIM, featuring OpenAPI-driven endpoints, containerized orchestration, and comprehensive monitoring.