Model Context Protocol

Demonstrates math tooling, multi-tool orchestration, Groq/LangGraph agents, and interactive chat with external tool integrations.

Django-based model context integration enabling seamless context propagation, dynamic adapters, and cross-service data sharing.

Python-based client-server suite enabling Claude-backed tool selection, arXiv and web search, plus iris prediction via a trained model.

Unique multi-server MCP demo with dynamic tool orchestration, AI tooling, and transport-agnostic clients.

A concise, interoperable model context manager with pluggable storage and lifecycle controls for multi-model workflows.

Python-based demonstration showcasing interactive tool endpoints, local LlamaIndex/Ollama integration, and a client-agent workflow for context-driven querying.

Extensible framework enabling LLMs to discover, connect, and orchestrate inbuilt and external tools.

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A lightweight Python tool to install and expose a model context workflow for Claude Desktop integration. Designed for fast onboarding and modular extension in research and product experiments.

Extensible MCP server enabling context-aware orchestration with pluggable backends and streamlined integration.

Standardized, versioned interface for exchanging model context between AI engines and orchestration layers, emphasizing portability and secure multi-tenant operations.

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Python-based MCP client using Claude 3.5 Sonnet for dynamic tool discovery and interactive tool-augmented reasoning.

Modular, secure, pluggable context management server for AI workflows with efficient serialization and isolation.

Native .NET server offering modular context management, pluggable storage, and interoperable endpoints for model context operations.

Modular MCP server with pluggable adapters, versioned contexts, and secure data exchange for reliable, extensible AI context management.

Comprehensive Python collection with modular tooling, resumable messaging, cancellation, and real-time updates for robust integrations.

A TypeScript-based server delivering dynamic model context with secure, scalable context management.

Dynamic model-context orchestration with secure isolation, pluggable adapters, and context-aware routing for advanced AI workflows.

Lightweight Python MCP server exposing /mcp on port 8000 via FastMCP for rapid model context handling.

Python-based MCP framework enabling LangGraph-driven multi-agent workflows with protocol-first orchestration for research assistants.

Platform powered by Model Context Protocol that combines Gemini 2.5 Flash with external data for geometry tasks, IBGE lookups, and intelligent guidance.

Go-based runtime coordinating model-context exchanges with plugin backends, enabling dynamic context management, streaming responses, and secure, scalable tool integration for complex AI workflows.