Model Context Protocol Example

Standardized context sharing enables coordinated multi-agent workflows with dynamic routing and evolving prompts.

Protocol-driven framework enabling seamless model-context integration, dynamic context shaping, cross-model coordination, and extensible plugin ecosystems.

Model Context Protocol

Open protocol unifying LLMs with diverse data sources and tools via streamlined middleware.

Model Context Protocol (MCP) is an open protocol that standardizes how applications provide context to LLMs- https://docs.anthropic.com/en/docs/agents-and-tools/mcp

Prototype detailing modular context handling and explanation flow for Python-based experiments.

Arxiv Knowledge Agent MCP Server enables Arxiv search, paper summarization, and modular MCP-based tooling via web and CLI.

Model Context Protocol Quickstart - server and client in the same repository.

Unified HuggingFace MCP server for Cursor enabling discovery, information access, and cross-task execution across text and vision models.

Proof-of-concept chat agent using direct document context, no embeddings, with Google Drive integration and keyword-based relevance.

Hands-on Anthropic MCP examples showcasing seamless prompt context orchestration with reusable blocks and evaluation hooks.

PlayWithMCP offers an interactive, modular environment to prototype and compare context-driven prompts, featuring shareable contexts and pluggable components for rapid experimentation.

Python-based testbed for exploring context-aware interactions and protocol iteration, enabling modular routing and lightweight experimentation.

Connects GitHub tools to Semantic Kernel, exposing actions as kernel functions for seamless LLM-driven automation.

A secure, real-time, bidirectional interface that standardizes connectivity between AI systems and external tools, APIs, and data sources.

Intelligent routing system that selects specialized AI models per query, boosting efficiency, cost-effectiveness, and accuracy through context-aware orchestration.

Desktop Claude integration with local context management and streamlined prompts.

AI monitoring tool for multiclass models focusing on data drift, data integrity, PSI tracking, and human-in-the-loop feedback.

test MCP, sqlLite and client

mcp server

An MCP (Model Context Protocol) server that exposes tools and data to AI models using a standard protocol, enabling secure and structured interaction between AI and external systems.

MCP server with Spring Boot

This is mcp server for mcp client function calling