Flexible Model Context Protocol integration to read Datagroom data and empower LLM-driven reasoning with dataset metadata.

Akademische Fallstudie zur kontextbasierten Anbindung externer Werkzeuge in KI-Anwendungen, mit zweistufigem Architekturkonzept.

TypeScript implementation enabling modular context provisioning and request routing via Model Context Protocol.

Python based modular framework with FastAPI backend, offline AI, and GUI for discovering devices and building dashboards in industrial environments, enabling local agent workflows.

Modular MCP server enabling easy tool registration, retrieval, and configurable tool metadata for Python projects.

Dynamic tool invocation and context-aware planning for intelligent, multi-agent workflows with OpenAI integration.

Next.js sample with Vercel adapter, SSE transport, Redis streaming, and Fluid compute optimization for scalable context handling.

Lightweight FastAPI-based context coordination for AI models with pluggable memory, secure access, and modular integration. It enables memory and tool sharing across agents, clean separation of concerns, and simple deployment for modern AI workflows. Onboarding is quick for teams adopting protocol patterns.

MCP Crash course

CLI-driven interactive chat with document-aware prompts and extensible tool integrations, designed for rapid experimentation and customization.

CLI-driven AI chat with document retrieval and extensible tool integrations via a modular protocol.

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

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.

Model_Context_Protocol MCP

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.

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.