Introduction

Anthropic’s Model Context Protocol (MCP) is gaining traction as a way to standardize how AI applications connect to external tools and data sources. Looking at the planned MCP Registry and the protocol itself, I noticed some familiar patterns from earlier integration standards.

The MCP Registry bears striking resemblances to UDDI (Universal Description, Discovery and Integration) from the early 2000s. Both aim to be centralized registries for service discovery, storing metadata to help developers find available capabilities. MCP’s approach to standardized data access also echoes Microsoft’s OData protocol.

We see this pattern frequently in technology—old concepts repackaged with new implementation approaches. Sometimes the timing or context makes all the difference between success and failure.

UDDI and MCP Registry: What’s Different This Time

UDDI tried to create a universal business service registry during the early web services era. Companies could theoretically discover and connect to each other’s services automatically. The concept was sound, but adoption remained limited.

MCP Registry targets a narrower scope—AI tool integrations—in an ecosystem that already has working implementations. More importantly, AI provides the missing piece that UDDI lacked: the “magic sauce” in the middle.

With UDDI, human developers still needed to understand service interfaces and write integration code manually. With MCP, AI agents can potentially discover services, understand their capabilities, and use them automatically. The AI layer handles the complexity that made UDDI impractical for widespread adoption.

OData: The Complexity Problem

OData never achieved broad adoption despite solving a real problem—standardized access to heterogeneous data sources. The specification became complex with advanced querying, batch operations, and intricate metadata schemas.

MCP deliberately keeps things simpler: tools, resources, and prompts. That’s the entire model. OpenAPI Specification closed some of OData’s gap, but you still can’t easily connect to an API programmatically and start using it automatically. MCP’s lower barrier to entry might be what was missing the first time around.

Timing and Context Matter

Several factors suggest MCP might succeed where its predecessors struggled:

AI as the Integration Layer: AI agents can handle the complexity that overwhelmed human developers with previous standards. They can discover services, understand capabilities, and generate appropriate calls automatically. As I discussed in “AI for Data (Not Data and AI)”, AI works as a synthesizer and translator – it doesn’t need perfect, pre-cleaned interfaces. This makes automatic integration practical in ways that weren’t possible with human developers manually writing integration code.

Proven Implementation First: Companies like Cloudflare and Atlassian already have production MCP servers running. This contrasts with UDDI’s “build the registry and they will come” approach.

Focused Problem Domain: Instead of trying to solve all integration problems, MCP focuses specifically on AI-tool integration. This narrower scope increases the chances of getting the abstraction right.

Simple Core Protocol: Three concepts versus OData’s extensive specification or SOAP’s complexity. The most successful protocols (HTTP, REST) stayed simple.

The Historical Pattern

We’ve seen this “universal integration layer” vision repeatedly:

  • CORBA (1990s): Universal object access
  • SOAP/WSDL (early 2000s): Universal web services
  • UDDI (early 2000s): Universal service discovery
  • OData (2010s): Universal data access
  • GraphQL (2010s): Universal query language
  • MCP (2020s): Universal AI integration

Each generation promised to solve integration complexity. Each had technical merit. Each faced adoption friction and complexity creep.

Why This Matters

The recurring nature of this pattern suggests the underlying problem is real and persistent. Integration complexity remains a significant challenge in software development.

MCP has advantages its predecessors lacked—AI as an intermediary layer, working implementations before standardization, and deliberate simplicity. Whether it can maintain focus and resist the complexity trap that caught earlier standards remains to be seen.

The next few years will be telling. If MCP stays simple and solves real problems, it might finally deliver on the promise of standardized integration. If it expands scope and adds complexity, it will likely follow the same path as its predecessors.