Exploring MCP Architecture for AECO Digital Workflows: A Practitioners Perspective

As an AECO consultant experimenting with the Model Context Protocol (MCP) paradigm, I've found fascinating parallels between optimal system architecture in AI and integrated design processes in our industry. The evolution of tool-using AI capabilities through MCP offers significant potential for transforming collaborative workflows and design processes.

Understanding MCP Architecture Choices

The documented approaches to MCP implementation, monolithic endpoints, granular per-tool endpoints, and domain-specific tool bundling, mirror familiar debates in our AECO digital ecosystem. Just as we balance centralized models against distributed specialist analyses, the MCP architecture requires thoughtful design decisions.

The evidence suggests domain-specific tool bundling provides optimal performance: "The more MCP servers you enable, the more you pollute your prompt with tool definitions, and the worse the results are" (Digital Systems, 2025). This aligns with our field's experience that excessive tool integration often creates diminishing returns.

AECO-Specific Applications

For AECO professionals, MCP implementation could support:

• Design Review Automation: Bundling visualization, code-checking, and clash detection tools within a single MCP endpoint allows AI to synthesize comprehensive design reviews without context switching.

• Project Documentation: Creating specialized endpoints for specification writing, quantity takeoff, and construction sequencing enables more efficient document generation while maintaining context efficiency.

• Field Connectivity: Separating field-specific tools from design tools reflects the natural workflow boundaries in construction projects.

Implementation Considerations

The microservice nature of MCP aligns with evolving AECO digital practices: "MCP embraces a microservices architecture, which involves breaking down the whole unit into small and easy-to-manage components" (Digital Systems, 2025). This approach supports the modular, iterative delivery increasingly demanded by project stakeholders.

For AECO-specific implementation:

• Phase-based grouping: Organize tools according to project lifecycle phases (planning, design, construction, operations)

• Security focus: Implement robust authentication to maintain data security across project teams

• Workflow alignment: Consider typical activity patterns when designing tool bundles

Conclusion

While still in early adoption stages, MCP architecture represents a significant opportunity for AECO technological integration. By applying the domain-specific bundling approach recommended in the research, we can enhance AI performance while maintaining the specialized capability needed across diverse project delivery activities.

The structured approach to MCP implementation mirrors best practices in execution planning, carefully balancing integration benefits against complexity costs. As we continue exploring this emerging paradigm, maintaining this balance will be essential for realizing genuine productivity improvements rather than merely adding technological complexity.