Browsing: MCP

As your AWS infrastructure scales, operational workflows naturally grow more complex. SREs and DevOps Engineers spend significant time context-switching between the AWS Management Console, CLI documentation,…

class RoutedAgent: def __init__(self, server: MCPToolServer, router: HybridMCPRouter, model: str): self.server = server self.router = router self.model = model def discover_exposed_tools(self, exposed_tool_names: List[str]) -> List[Dict[str, Any]]:…

Model Context Protocol (MCP) adoption has accelerated rapidly since its introduction in November 2024. Enterprises now manage dozens to hundreds of MCP servers—tools that extend AI…

When AI agents connect to tools through the Model Context Protocol (MCP), they gain access to capabilities that range from database queries and API calls to…

Stateful MCP client capabilities on Amazon Bedrock AgentCore Runtime now enable interactive, multi-turn agent workflows that were previously impossible with stateless implementations. Developers building AI agents…

Amazon Bedrock AgentCore Gateway provides a centralized layer for managing how AI agents connect to tools and MCP servers across your organization. It consolidates authentication, observability,…

In the development of autonomous agents, the technical bottleneck is shifting from model reasoning to the execution environment. While Large Language Models (LLMs) can generate code…

Teams across companies lose meeting notes and action items after discussions. This guide builds a lasting fix: an AI Meeting Summarizer and Action Planner using Claude…

import asyncio import json import io import contextlib import re from dataclasses import dataclass from typing import Callable, Awaitable import nest_asyncio nest_asyncio.apply() TOOL_DEFINITIONS = [ {…

Google has officially released the Colab MCP Server, an implementation of the Model Context Protocol (MCP) that enables AI agents to interact directly with the Google…