#model-context-protocol

#model-context-protocol

The Model Context Protocol (MCP) is an open standard from Anthropic, designed to facilitate seamless integration between AI models and external systems. By using standardized interfaces, MCP enables AI coding assistants to interact with various tools, such as version control systems, CI/CD pipelines, and even web browsers, without requiring native support for each integration. MCP ensures extensibility and interoperability, making it a flexible solution for developers who need AI-powered coding assistance beyond predefined environments.

A research team from Stanford University has released Paper2Agent, a framework that automatically converts scientific papers into interactive AI agents. The system, introduced in a recent paper, aims to make research methods more accessible by transforming traditional publications into dynamic entities that can execute analyses, reproduce results, and respond to new scientific queries through natural language interaction. Paper2Agent builds on the Model Context Protocol (MCP), a standard that allows large language models to connect with external tools and datasets.

According to OpenAI CEO Sam Altman, AgentKit is a visual tool for quickly building AI agents. "AgentKit is a complete set of building blocks available in the OpenAI platform designed to help you take agents from prototype to production. It is everything you need to build, deploy, and optimize agent workflows." He further states that AI is becoming increasingly capable, from a system you can ask anything to a system that can do everything for you. According to Altman, there is a lot of talk about AI agents, but they remain underutilized. He wants to solve that with AgentKit.

We already see that there is now a shortage of people who possess MCP skills that we are hiring for,

OpenAI has added a beta of Developer mode to ChatGPT, enabling full read and write support for MCP (Model Context Protocol) tools, though the documentation describes the feature as dangerous. Developer community lead Edwin Arbus said that "in developer mode, developers can create connectors and use them in chat for write actions (not just search/fetch). Update Jira tickets, trigger Zapier workflows or combine connectors for complex automations." Limitations in the initial beta are that developer mode does not work in Team workspaces or in project chats.

Large language models (LLMs) are much the same. They carry vast general knowledge yet lack the specific context that makes them immediately valuable to your organization. Just like new hires go through the onboarding ropes, LLMs need structured access to your business's data, tools, and workflows to become truly useful. That's where Model Context Protocol (MCP) comes in. MCP enables communication between AI applications, AI agents, applications and data sources.

Every time a developer wants their AI agent to use a new tool, like a weather API or a flight booking system, they have to build a custom bridge. It's like needing a different, clunky adapter for every single device you own.

GitLab has launched the public beta of its GitLab Duo Agent Platform, an orchestration tool that enables developers to collaborate asynchronously with AI agents across the DevSecOps lifecycle. The platform, now available to GitLab.com Premium and Ultimate customers as well as self-managed installations, transforms traditional, linear development workflows into dynamic, multi-agent systems where AI handles routine tasks such as refactoring, security scanning, and research, while developers focus on complex problem-solving.

For years, APIs have served as the backbone of data access, but they were never designed with AI in mind. They lack memory, context, and intent awareness-forcing developers to bolt on brittle glue code every time models change. Anthropic's introduction of MCP earlier this year marked a turning point, offering a standardized way to make APIs context-aware and AI-ready. But as Chivukula points out, adopting MCP isn't just about creating a one-off server.

Wassette is a new open source tool that allows developers to assemble lightweight Model Context Protocol servers using WebAssembly components, enhancing AI-driven functionalities.

The Model Context Protocol (MCP) enhances AI copilots by providing structured tools and context that enable effective task execution, beyond simple prompts.

The Model Context Protocol (MCP) is an open standard that enables large language models to interact with external tools and data through a standardized interface.

Agent Mode combines familiar and novel elements, facilitating three levels of AI collaboration: conversational brainstorming, human-approved code changes, and a near-autonomous coding agent.

Technologies like Model Context Protocol (MCP) are reshaping both how we build AI-powered applications and what we can do with them. MCP makes it easier to add AI features to existing code and services.

CloudBees launched Model Context Protocol Server to enhance its Unify platform, allowing integration of AI agents into DevOps workflows across multiple CI/CD platforms.

PayPal's new Agent Toolkit integrates its API suite with AI frameworks, enabling developers to streamline workflows like order management and invoice generation without complex setups.