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From Embedded Utility to Pluggable Backend Abstraction

· 3 min read
Chisanan232
Chisanan232
This project Maintainer

When I first added message queue support to the Chisanan232/slack-mcp-server project, it lived as a single helper tucked inside that repository. The job was simple: receive Slack webhook events, queue them, and let the rest of the system process them asynchronously. It worked well enough, but I quickly ran into the limits of keeping that logic embedded in one codebase.

One Project, One Off​

In the early days the queue consumer sat directly inside the Slack MCP server. Each time the service needed to talk to Redis I duplicated config helpers, re-implemented error handling, and kept the queue contracts tightly coupled to Slack-specific abstractions. It felt like a minor compromise—until I began building another MCP service.

Copy-Paste or Better Architecture?​

While building Chisanan232/clickup-mcp-server, I faced the exact same webhook-to-queue flow. Copying the Slack implementation would have been fast but painful to maintain. Every bug fix, performance patch, or provider enhancement would require a multi-repo patch dance. That experience pushed me to rethink how backend components should be shared across projects.

Designing for Pluggability​

Instead of cloning code, I teased the queue logic into a reusable abstraction layer:

  • Protocol-first contracts define the queue behaviours any provider must satisfy.
  • Dynamic discovery via entry points means a simple pip install activates a new backend.
  • Symmetric uninstall lets teams remove a provider with pip uninstall when it is no longer needed.
  • Application-facing helpers keep consumer code stable even as the concrete backend changes.

With these pieces in place, switching from Redis to Kafka or an AWS-managed queue becomes a packaging decision, not an application rewrite.

Centralising Backend Components​

The extracted library, now this abstract-backend project, turned into a home for reusable backend behaviours:

  • Shared logging utilities and types ensure consistent diagnostics.
  • Contract tests validate that every provider honours the same guarantees.
  • Documentation and examples show how to build new providers or adopt existing ones.

Most importantly, every Python project in this ecosystem now speaks to the same protocols. Teams can experiment with different providers without touching application workflows.

Looking Ahead​

Abstract Backend started as a humble helper function and grew into a modular architecture because I needed flexibility across multiple MCP services. The journey taught me that decoupling backend components into their own package keeps projects maintainable, encourages experimentation, and makes upgrades safer. I hope this story helps other developers avoid the copy-and-paste trap and embrace pluggable backends from day one.