December 12, 2025

MCP Weekly: Agentic AI Foundation, Cloud Momentum, and New Security Tools

How open governance, cloud rollouts, and new security tools are maturing MCP

Welcome to the latest installment of the MCP Weekly digest, covering the major developments related to the Model Context Protocol (MCP) from December 4th to December 11th, 2025, focusing on major industry governance, widespread cloud provider adoption, and new developer tooling with built-in security.

TL;DR

This week marked a major milestone for MCP. Anthropic donated MCP to the Linux Foundation, resulting in the formation of the Agentic AI Foundation (AAIF), a neutral governance body backed by AWS, Google, and Microsoft. Alongside MCP, Block’s goose (agent workflows) and OpenAI’s AGENTS.md (coding agent guidance) were also contributed, strengthening the foundation’s standards portfolio.

Cloud adoption accelerated immediately. Google announced full MCP support across core services including Maps and BigQuery, while AWS doubled down with serverless hosting for MCP servers and new protocol features for long-running tasks and user elicitation.

The ecosystem filled in fast. New releases focused on security, monitoring, and developer tooling, including end-to-end MCP server security solutions and specialized servers for observability and code analysis.

Major Updates of the Week

Foundational Governance and Open Standards

The Linux Foundation established the Agentic AI Foundation (AAIF) this week to provide a neutral, open structure for the future of autonomous AI systems. This move signals the industry's shift from proprietary, closed systems to open, shared infrastructure.

Placing the MCP under this open governance structure solves a significant industry problem, ensuring the protocol remains stable, transparent, and not controlled by a single company. This allows developers and enterprises to invest in MCP with confidence.

‍

Vendor / Project	Key Area	Significance
Anthropic	Donated the Model Context Protocol to the AAIF	Establishes MCP as the universal, vendor-neutral standard for connecting AI models to tools and data, solving the “n × m integration problem.”
Block	Contributed the goose framework to the AAIF	Provides an open-source framework for building reliable, structured AI agent workflows on top of MCP.
OpenAI	Contributed the AGENTS.md standard to the AAIF	Gives AI coding agents consistent, project-specific guidance so their behavior is predictable across repositories.

Major Cloud Provider Adoption

Both Google and AWS announced significant expansions of their commitment to MCP, validating its role as the core integration standard for enterprise-scale AI. This unification makes it easier for technical leaders to deploy AI agents that reliably interact with production systems, shifting focus from model training to solving real business problems.

‍

Vendor / Product	Key Action	Significance
Google Cloud	Announced full MCP support for core services	Creates a unified, enterprise-ready integration layer across Google and Google Cloud, enabling sophisticated multi-step AI workflows.
Google Maps	Rolled out initial MCP support (Grounding Lite)	Gives agents access to trusted, up-to-date geospatial data, reducing hallucinations in location-based queries.
Google BigQuery	Added MCP support for native schema interpretation	Allows AI agents to query large enterprise datasets in place, improving both security and performance.
AWS	Reaffirmed an “all-in” commitment to MCP	Provides long-term confidence in the protocol and offers serverless hosting for custom MCP servers via Amazon Bedrock AgentCore.
AWS	Contributed Tasks and Elicitations features to MCP	Enables long-running, asynchronous agent operations and structured user clarification when required.

Specialized Tooling and Security for MCP

As MCP adoption accelerates, the ecosystem is quickly filling in the gaps required for real-world usage. Vendors are focusing less on experimentation and more on the hard problems of security, observability, cost control, and developer experience. This week’s releases show how MCP servers are evolving into production-grade components that can be governed, audited, and safely deployed inside enterprise environments.

‍

Vendor / Product	Key Action	Significance
Backslash Security	Launched an end-to-end security solution for MCP servers	Addresses data leakage and prompt injection risks through a real-time MCP proxy that enforces governance and control.
AWS DevOps Agent & Datadog	Integrated AWS Agent with the Datadog MCP Server	Reduces incident resolution time from hours to minutes by securely correlating monitoring data across systems.
Amazon Prometheus	Released an open-source MCP Server	Removes the PromQL learning curve by enabling natural-language queries over real-time monitoring data.
RubyMine IDE	Introduced a Rails-aware MCP Server	Automatically understands Rails project structure, improving speed and reliability of AI-assisted development.
Rails MCP Server	Refactored for progressive tool discovery	Reduced context usage by approximately 67%, lowering model costs and improving execution speed.
BrowserStack	Released its MCP Server in the AWS Marketplace	Allows developers to control real-device testing workflows via natural language directly from AI assistants.

‍

My Thoughts: A Shift Toward Deeper MCP Adoption

This week’s updates make it clear that MCP has crossed an important threshold. What stood out is not just the governance shift with the Linux Foundation, but how quickly the rest of the ecosystem responded. Cloud providers, tooling vendors, and platform teams all moved in parallel, which usually only happens once a standard is seen as stable enough for real production use. The announcements this week felt less like experimentation and more like coordination.

Looking ahead, the next phase seems obvious. With governance settled and cloud support in place, the focus will shift toward deeper adoption. That means stronger security controls, better observability, clearer operational patterns, and more opinionated agent frameworks built on top of MCP. As these pieces mature, building and running AI agents will start to look much more like standard software engineering rather than frontier AI research.

‍

Om Shree

Technical Evangelist

About Om Shree

Om Shree is a researcher, technical writer, and AI evangelist who focuses on making complex AI and agent workflows easier to understand. Om's passion is breaking down emerging technologies into clear, practical insights. He's excited to provide useful in-depth research that supports product planning and helps developers navigate new tools and systems with ease.

Customized Plans for Real Enterprise Needs

Gentoro makes it easier to operationalize AI across your enterprise. Get in touch to explore deployment options, scale requirements, and the right pricing model for your team.

Get in Touch