Will MCP become a standard? A look at what's coming

Source: Dev.to

Introduction

Anthropic released MCP (Model Context Protocol) and suddenly everyone’s talking about it. The real question is: will it stick, or will it become another protocol that fades into obscurity?

Why a Standard Is Needed

Every AI app faces the same problem: how to give AI access to tools.

• OpenAI uses function calling.
• LangChain has its own tools format.
• Each startup builds a custom integration layer.

This fragmentation means tools built for one system don’t work elsewhere. MCP aims to solve this with a standard protocol: build once, use everywhere.

Lessons from Past Standards

Successful standards share common traits:

• HTTP – originated at CERN, later standardized by the W3C.
• JSON – championed by Douglas Crockford, eventually standardized by ECMA.
• USB – driven by Intel and a broader consortium.

MCP has Anthropic behind it— not the biggest player, but credible, and it’s open‑sourced rather than proprietary.

MCP Overview

• Simplicity: similar to REST, which won partly because it’s easy to adopt.
• Protocol: JSON‑RPC over stdio or Server‑Sent Events (SSE).
• Standard methods: tools/list, tools/call.
• Schema: clear definition for tools.

You can implement a basic MCP server in an afternoon.

Current Pain Points in AI‑Tool Integration

• Custom integrations for each AI provider.
• No standard way to discover tools.
• Duplicate work across projects.

MCP eliminates these issues: one protocol, all AI agents.

Adoption Landscape

AI Providers

• Claude (native support)
• Cursor (IDE integration)
• More providers are expected to follow

Tool Builders

• Growing ecosystem of MCP servers.
• Open‑source implementations are popping up.
• Companies are building MCP integrations.

Developers

• Building local MCP servers for personal use.
• Wrapping existing APIs as MCP tools.
• Sharing configurations and tools.

Early signs are promising, though it’s not mainstream yet.

Comparison with Alternatives

MCP’s advantage is its vendor‑agnostic design, aiming for universal adoption.

Risks and Scenarios

• Competing standards: If OpenAI releases a better‑marketed protocol, MCP could lose momentum.
• Multiple protocols: A “VHS vs. Betamax” situation would force developers to support several standards, undermining the “build once, use everywhere” promise.
• Complexity creep: Adding too many features could make MCP hard to implement correctly. So far, it remains simple.
• Niche outcome: MCP might stay limited to Anthropic users, never reaching critical mass.

Future Outlook

• 2025 – MCP adoption grows among Claude users; OpenAI may announce compatibility or a competing standard.
• 2026 – Major APIs (GitHub, Stripe, Slack, etc.) ship MCP servers alongside REST. Framework support matures.
• 2027 – MCP becomes assumed; lacking MCP support will be akin to lacking a REST API today.

Implications If MCP Becomes Standard

For Developers

• Build tools once, work everywhere.
• No vendor lock‑in.
• Access to a growing ecosystem.

For Companies

• Standard way to expose services to AI.
• Reduced integration costs.
• Ability to reach all AI agents, not just one.

For Users

• AI assistants that can actually perform tasks.
• Consistent experience across tools.
• More capable AI interactions.

Getting Started

• Learn the protocol basics.
• Build a few MCP tools.
• Wrap existing APIs as MCP tools.

Tools like Gantz Run make this easy—spin up an MCP server in minutes, not days. The cost of experimenting is low; the cost of being late is high.

Conclusion

Betting on MCP? Or waiting to see how it plays out? The community’s feedback will shape its future.