Browsing: MCP

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…

In recent times, many developments in the agent ecosystem have focused on enabling AI agents to interact with external tools and access domain-specific knowledge more effectively.…

Liquid AI has released LFM2-24B-A2B, a model optimized for local, low-latency tool dispatch, alongside LocalCowork, an open-source desktop agent application available in their Liquid4All GitHub Cookbook.…

Image by Editor   # Introducing MCP  Standards succeed or fail based on adoption, not technical superiority. The Model Context Protocol (MCP) understood this from the start.…

Amazon Quick supports Model Context Protocol (MCP) integrations for action execution, data access, and AI agent integration. You can expose your application’s capabilities as MCP tools by…

Image by Author   # Introduction  The Model Context Protocol (MCP) has changed how large language models (LLMs) interact with external tools, data sources, and services. However,…

Building AI agents is the new gold rush. But every developer knows the biggest bottleneck: getting the AI to actually communicate to your data. Today, travel…

AI agents are rapidly evolving from mere chat interfaces into sophisticated autonomous workers that handle complex, time-intensive tasks. As organizations deploy agents to train machine learning…

AI agents are no longer just answering questions. They read code, deploy apps, run workflows, move money, search the web, and remember what happened last week.…