When OpenAI adopted MCP in March 2025, followed by Google's confirmation in April, skeptics called it middleware hype. By February 2025, over 1,000 community-built connectors existed. Microsoft veteran Steven Sinofsky dismissed it as middleware that "never quite lives up to promises in practice."
The adoption numbers tell a different story. They show MCP solves a real problem elegantly.
At TinyFish, we build enterprise web agent infrastructure. That work means evaluating protocols not by adoption momentum, but by what architectural choices enable. MCP's rapid ecosystem growth proves something specific: the cooperative data source problem is real, valuable, and MCP's architecture handles it well.
The Problem MCP Actually Solves
Organizations need AI assistants to access their data infrastructure. Not web surfaces, but databases, code repositories, business tools. Systems that want to be queried. MCP provides standardized integration for exactly this use case.
The architecture works. JSON-RPC 2.0 over STDIO or HTTP. Servers expose specific capabilities: database access, repository queries, tool integrations. Authentication through OAuth tokens. State management through API keys. Request-response patterns.
MCP solves the cooperative data source problem. Its architecture assumes environments where authentication is standardized, connections are stateless, and both sides want integration to succeed.
This works because both sides want the connection to succeed. Your database is designed to provide information. Your code repository has APIs built for integration. Your business tools expect programmatic access. The protocol optimizes for environments where integration is the goal, not an adversarial challenge.
The November 25th specification update strengthens this foundation: async operations for long-running tasks, server discovery through the MCP Registry, standardized extensions for specialized domains. These improvements make MCP better at what it was designed for: production-grade integration between AI systems and cooperative data sources.
What Validation Looks Like
The 1,000+ connectors that emerged by February aren't just ecosystem activity. They're evidence that standardized AI-to-data-source integration solves real operational problems. Organizations were building custom integrations for every data system. MCP provides a common protocol. The market responded.
Major platform adoption from OpenAI, Google, and Microsoft validates the architectural approach. These companies evaluated MCP's design and chose it over proprietary alternatives. Call it what you want, but when three major platforms independently reach the same conclusion, they're recognizing that the protocol handles its intended use case well.
From an infrastructure perspective, we look at what problems protocols solve and what assumptions they make about operating environments. MCP solves the cooperative data source problem. Its architecture assumes environments where authentication is standardized, connections are stateless, and both sides want integration to succeed.
Design clarity, not limitation. The protocol does what it was built to do, and adoption numbers confirm organizations need exactly that capability. MCP's success validates both the problem and the solution.