TL;DR — The Short Answer
The Model Context Protocol (MCP) — the "USB-C of AI" — became the standard way to connect AI models to tools and data in 2026. By mid-year it ran on 10,000+ enterprise servers with around 97M SDK downloads. For PMs, interoperability (MCP plus Google's Agent2Agent protocol) is now a roadmap decision, not an engineering footnote.
Key Takeaways
| Point | What it means | The number to cite |
|---|---|---|
| What it is | Open standard connecting AI to tools & data | Called the "USB-C of AI" |
| Scale | Enterprise servers running MCP | 10,000+ servers, ~97M SDK downloads |
| Momentum | Usage growth (Firecrawl, mid-2026) | ~35% month-over-month |
| Companion | Google Agent2Agent (A2A) | Handles multi-agent orchestration |
What MCP Is
MCP (Model Context Protocol) is an open standard for connecting AI models to external tools and data sources. It is often called the "USB-C of AI" because it standardizes the plug: instead of building a custom integration for every model-to-tool connection, you implement MCP once and it works across products.
MCP by the Numbers
Adoption moved fast through the first half of 2026.
10,000+
Enterprise servers running MCP
~97M
MCP SDK downloads
~35%
Month-over-month usage growth (Firecrawl)
MCP
Connects models to tools & data — the integration layer.
Agent2Agent (A2A)
Google's protocol for multi-agent orchestration.
Why It Is a PM Decision
For product managers building anything agentic, the interoperability layer is now strategic. What your product can connect to — and what can connect to it — determines whether you are part of the AI ecosystem or a closed island. Google's Agent2Agent (A2A) protocol handles multi-agent orchestration alongside MCP's tool access, so the two together define how your product participates.
What PMs Should Do
- Map your integration surface. List the tools and data your product exposes or consumes.
- Decide your stance on MCP. Will you publish an MCP server, consume MCP tools, or both?
- Consider A2A for multi-agent features. If agents need to coordinate, orchestration is part of the design.
- Tie it to the agent roadmap. See scoping agents that ship.
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Every figure links to its primary reporting. Dates reflect the June 2026 news cycle.
Frequently Asked Questions
What is MCP (Model Context Protocol)?
MCP is an open standard for connecting AI models to external tools and data sources — often described as the "USB-C of AI." It standardizes how a model discovers and calls tools, so integrations are reusable across products rather than custom-built each time.
How widely adopted is MCP in 2026?
By mid-2026, MCP was implemented on more than 10,000 enterprise servers with around 97 million SDK downloads. Some platforms reported MCP usage growing roughly 35% month-over-month.
What is the difference between MCP and Agent2Agent (A2A)?
MCP connects a model to tools and data — the integration layer. Google's Agent2Agent (A2A) protocol handles communication and orchestration between multiple agents. They are complementary: MCP for tool access, A2A for multi-agent coordination.
Why should a product manager care about MCP?
Because interoperability is now a roadmap decision, not just an engineering detail. What your product can connect to — and what can connect to it — is a strategic choice. Supporting MCP can make your product part of the broader AI ecosystem rather than a closed island.
Is MCP only relevant for AI products?
It is most relevant for products that expose tools or data to AI systems, or that build agentic features. But as more software is accessed through AI assistants and agents, MCP support increasingly affects discoverability and integration even for non-AI-first products.
About the Author
Aditi Chaturvedi
·Founder, Best PM JobsAditi is the founder of Best PM Jobs, helping product managers find their dream roles at top tech companies. With experience in product management and recruiting, she creates resources to help PMs level up their careers.