If your developers or IT architects have started throwing around the acronym MCP, you are not alone. In the past six months, the Model Context Protocol has gone from a niche Anthropic open-source project to the universal lingua franca of enterprise AI integration. Every major AI vendor supports it. The first dedicated developer summit drew 1,200 attendees. Monthly SDK downloads crossed 97 million.

This is not hype. It is a structural shift in how AI agents connect to enterprise systems - and it has real implications for every organization building or evaluating AI.

What MCP Actually Is

MCP stands for Model Context Protocol. Anthropic introduced it in November 2024 as an open standard for connecting AI applications to external systems: databases, APIs, internal tools, SaaS platforms, and anything else an AI agent might need to do useful work.

Before MCP, every AI integration was custom work. If you wanted an AI assistant to query your CRM, read from your knowledge base, and submit a ticket to your ITSM platform, someone had to write three separate integrations - and maintain them every time the underlying systems changed.

MCP defines a common architecture: an MCP Host (the AI application), an MCP Client (a translator layer), and MCP Servers (the data sources and tools the agent can use). Build an MCP Server once for your CRM, and any MCP-compatible AI can use it. The same way HTTP standardized how web browsers talk to web servers, MCP is standardizing how AI agents talk to everything else. The standard is now governed by the Linux Foundation’s Agentic AI Foundation, with more than 100 member organizations.

The Adoption Numbers Are Meaningful

Adoption has been fast. In March 2026, MCP crossed 97 million monthly SDK downloads - a 970x increase from its November 2024 launch. By April 2026, more than 17,000 MCP servers were indexed across public registries, with 41% of enterprise AI teams running at least one custom internal MCP server not listed publicly.

More important than the download numbers is vendor breadth. MCP support is now confirmed across Claude, ChatGPT (via the Agents SDK), Google Gemini API, Vertex AI Agent Builder, Microsoft 365 Copilot, GitHub Copilot, and every major AI development environment including Cursor, Windsurf, and JetBrains AI Assistant. The Agentic AI Foundation’s first MCP Dev Summit North America, held in April 2026 in New York, drew 1,200 attendees and 95 sessions from co-founders, maintainers, and teams running MCP in production.

When every major vendor ships native support and 78% of enterprise AI teams report at least one MCP-backed agent in production, a standard stops being an option and starts being an expectation.

What the GitHub MCP Registry Changes

In September 2025, GitHub launched the GitHub MCP Registry - a curated, publicly searchable catalog of MCP servers contributed by major software vendors. Launch partners included Figma, Postman, HashiCorp, and Dynatrace. The registry solves a friction point that slowed early MCP adoption: discovery. Instead of searching GitHub repositories or asking your AI vendor which integrations exist, there is now a single trusted source.

For enterprise teams, this means evaluating AI integration options is faster. If your stack includes tools already listed in the registry, you can connect them to your agents without writing custom code. If you have internal systems not covered, you build an MCP server once and reuse it across all your AI platforms going forward.

Microsoft Agent 365 and the M365 Integration Story

For organizations running Microsoft 365, the MCP story is particularly immediate. Microsoft Agent 365 - now generally available as of May 2026 - provides pre-certified MCP servers covering Outlook, Teams, SharePoint, OneDrive, Word, Dataverse, and Dynamics 365 Sales and Service modules.

This means a custom enterprise agent built on any MCP-compatible framework can access M365 data through standard, governed interfaces rather than custom Microsoft Graph API integrations. Microsoft Defender will also begin mapping asset context for each agent - including which MCP servers it has configured and which cloud resources it can reach - extending its existing security architecture to cover the agentic layer.

For IT and security teams, this matters. Agentic AI in your M365 environment is no longer an opaque capability. It has an auditable integration surface with defined access controls.

Real Enterprise Use Cases

The practical value of MCP becomes clear in multi-system workflows already running in production:

Talent acquisition: An AI agent connected to an ATS via MCP can synthesize interview feedback, cross-reference HRIS skill gap data, pull profile information, and draft an offer recommendation - without a human manually moving data between five separate systems.

Financial operations: Finance teams are deploying read-only ERP MCP servers so agents can answer questions about budget status, variance analysis, and purchase order history without requiring analysts to pull manual reports.

Employee onboarding: Workflows that previously required HR coordinators to manually trigger steps across multiple platforms can be orchestrated by a single agent connected to each system via MCP, with human approval gates preserved at the decision points that carry risk.

The shared pattern: MCP removes the integration tax. Effort that used to go into wiring systems together goes into defining workflows and governance rules instead. Deployments that previously took quarters now take weeks.

Security and Governance Are Not Optional

MCP makes integration easier, which means the attack surface grows with adoption. Each MCP server your agents can access is a potential privilege escalation path if not properly governed. The fundamentals are straightforward but require deliberate implementation:

Access control: MCP servers should implement role-based access controls scoped to each agent identity. An agent handling customer support should not have the same data access as one running financial reconciliation.

Audit logging: Every MCP call - what tool was invoked, what parameters were passed, what was returned - should be logged. This is required for compliance and for incident investigation when something goes wrong.

Input validation: MCP servers should validate all inputs before passing them downstream. Prompt injection via MCP tool calls is a real attack vector, not a theoretical one.

Human-in-the-loop gates: For workflows touching financial, legal, or sensitive customer data, human approval steps should not be bypassed for agent convenience.

The agent identity governance post on this blog covers the broader question of treating agents as first-class identity principals - the same principles apply directly to how you scope MCP server access and manage agent credentials.

What to Do Now

If you are still evaluating agentic AI platforms, MCP support should be a hard requirement in your architecture decisions. Choosing tools with native MCP support now avoids expensive integration rework in 12-18 months. Any custom internal tooling you build should be structured as MCP servers from the start, making them reusable across whatever AI platforms you adopt.

If you are already running agents in production, audit your current integrations against MCP availability. Many of your custom integrations likely have MCP equivalents in the GitHub registry or your vendor’s catalog that would be more maintainable.

The window where ignoring MCP is a neutral choice is closing. Within 18 months, asking whether a platform supports MCP will be as standard as asking whether it has an API.

For help assessing your current AI integration architecture and building an MCP readiness plan, use the contact form to start the conversation.