Protocol
Building Advanced MCP (Model Context Protocol) Agents with Multi-Agent Coordination, Context Awareness, and Gemini Integration
class MCPAgent: “””Advanced MCP Agent with evolved capabilities – Jupyter Compatible””” def __init__(self, agent_id: str, role: AgentRole, api_key: str = None): self.agent_id = agent_id self.role = role self.api_key = api_key self.memory = [] self.context = AgentContext( agent_id=agent_id, role=role, capabilities=self._init_capabilities(), memory=[], tools=self._init_tools() ) self.model = None if GEMINI_AVAILABLE and api_key: try: genai.configure(api_key=api_key) self.model = genai.GenerativeModel(‘gemini-pro’) print(f”✅…
Is Model Context Protocol MCP the Missing Standard in AI Infrastructure?
The explosive growth of artificial intelligence, particularly large language models (LLMs), has revolutionized how businesses operate, from automating customer service to enhancing data analysis. Yet, as enterprises integrate AI into core workflows, a persistent challenge emerges: how to securely and efficiently connect these models to real-world data sources without custom, fragmented integrations. Introduced by Anthropic…
Model Context Protocol (MCP) FAQs: Everything You Need to Know in 2025
The Model Context Protocol (MCP) has rapidly become a foundational standard for connecting large language models (LLMs) and other AI applications with the systems and data they need to be genuinely useful. In 2025, MCP is widely adopted, reshaping how enterprises, developers, and end-users experience AI-powered automation, knowledge retrieval, and real-time decision making. Below is…
Model Context Protocol (MCP) for Enterprises: Secure Integration with AWS, Azure, and Google Cloud- 2025 Update
The Model Context Protocol (MCP), open-sourced by Anthropic in November 2024, has rapidly become the cross-cloud standard for connecting AI agents to tools, services, and data across the enterprise landscape. Since its release, major cloud vendors and leading AI providers have shipped first-party MCP integrations, and independent platforms are quickly expanding the…
How to Use python-A2A to Create and Connect Financial Agents with Google’s Agent-to-Agent (A2A) Protocol
Python A2A is an implementation of Google’s Agent-to-Agent (A2A) protocol, which enables AI agents to communicate with each other using a shared, standardized format—eliminating the need for custom integration between services. In this tutorial, we’ll use the decorator-based approach provided by the python-a2a library. With simple @agent and @skill decorators, you can define your agent’s…
A Step-by-Step Coding Guide to Defining Custom Model Context Protocol (MCP) Server and Client Tools with FastMCP and Integrating Them into Google Gemini 2.0’s Function‑Calling Workflow
In this Colab‑ready tutorial, we demonstrate how to integrate Google’s Gemini 2.0 generative AI with an in‑process Model Context Protocol (MCP) server, using FastMCP. Starting with an interactive getpass prompt to capture your GEMINI_API_KEY securely, we install and configure all necessary dependencies: the google‑genai Python client for calling the Gemini API, fastmcp for defining and…
What is TCP/IP Model and How Does The Protocol Work – SitePoint
Seamless network communications between devices is pivotal in today’s digital era. The TCP/IP model, a fundamental framework in networking, makes this possible by ensuring data is sent and received accurately across the Internet. From browsing websites to sending emails, TCP/IP silently powers these operations. This article dives into the TCP and IP model, its importance,…
Meet Skyseed, a VC fund and incubator backing the Bluesky and AT Protocol ecosystem | TechCrunch
On November 15, Peter Wang posted a message requesting ideas for a new incubator and fund to support experimental projects built on the burgeoning Bluesky/AT Protocol ecosystem. Four weeks later, Skyseed emerged with an initial commitment of $1 million. This turnaround, a speed underscored by the fact that the fund doesn’t even have a website…