The Problem Nobody Talks About
Distributed transactions fail partway through. This isn't hypothetical - it's production reality at scale. When Asana's MCP breach exposed 1,000 customers' data for 34 days, the root cause wasn't malice. It was architectural: systems diverged, and reconciliation failed.
Here's what happens: An LLM orchestrates a hotel checkout. Stripe charges $250 successfully. Then Salesforce times out updating the booking. The guest paid, but three related objects - booking, room status, sales opportunity - remain stale. No shared transaction boundary exists. Manual reconciliation: 30+ minutes per incident.
95% of AI projects fail at the integration layer, not because LLMs aren't capable, but because nobody solved partial failure recovery.
Why Traditional Error Handling Fails
MCP distinguishes between protocol errors and execution failures. Neither category explicitly handles partial success. A try/catch block can't distinguish between:
- Failed before payment → safe to retry
- Failed after payment → need refund or idempotent retry
- Failed during Salesforce update → need reconciliation to determine state
Error handling is binary. Distributed workflows have partial success.
The Saga Pattern Response
Enterprises are adapting distributed transaction patterns - specifically saga pattern compensation logic - to MCP tool design. The approach: build consistency guarantees into backend systems, not LLM reasoning.
Key architectural shifts:
Idempotency at the tool layer. Each operation must handle retries without duplicating state changes. The outbox pattern - recording intended state changes before execution - provides a reconciliation log when failures occur mid-workflow.
Compensation transactions. When Stripe succeeds but Salesforce fails, the system needs explicit rollback logic. Not "retry until it works" - that compounds inconsistency with duplicate charges and orphaned records.
Small, composable tools. One documented case: reducing tool granularity and implementing compensation patterns achieved 94% success rates with 2-4 second response times, down from multiple retry loops.
The Separation of Concerns
The emerging consensus represents a significant shift from early MCP deployments that pushed complexity onto the model layer. The architecture must guarantee consistency; the LLM shouldn't need to reason about failure recovery.
This isn't theoretical. Of 5,960+ MCP servers in the ecosystem, approximately ten are trustworthy for enterprise use. Successful deployments follow a six-month prototype-to-production path, not "deploy in days" marketing claims.
What This Means In Practice
MCP doesn't belong in production databases or real-time systems - it's suited for background analysis and decision support. When workflows span payment gateways, CRMs, and inventory systems, you need distributed transaction patterns that existed before LLMs: saga orchestration, compensating transactions, idempotent operations.
The trade-off is complexity. But the alternative - manual reconciliation at scale - is worse. History suggests that architectures which ignore partial failure eventually learn this lesson expensively.