Model Context Protocol for Drupal

As AI tools become increasingly integrated into development workflows, a fundamental challenge has emerged: how do AI assistants effectively interact with complex, domain-specific systems like content management platforms? Model Context Protocol (MCP) offers an elegant solution that’s particularly relevant for Drupal developers.

What is Model Context Protocol?

Model Context Protocol (MCP) is an open protocol developed by Anthropic that standardizes how AI assistants connect to external data sources and tools. Think of it as a universal adapter that allows AI models to interact with your systems in a structured, secure, and contextually aware manner.

At its core, MCP solves a critical problem: AI models are powerful at processing information and generating responses, but they need a reliable way to access the specific context and tools relevant to your work.

The Three Pillars of MCP

MCP is built around three key concepts:

1. Resources: Structured data that AI can read and understand (files, database records, API endpoints, documentation)
2. Prompts: Pre-defined interaction patterns that help AI understand how to work with your system
3. Tools: Actions the AI can take (running commands, creating content, modifying configurations)

Together, these pillars enable AI assistants to move beyond generic responses to become context-aware partners in complex development tasks.

Why Context Matters: The Challenge of Complex Systems

Consider a typical Drupal development scenario: you’re building a custom module that needs to integrate with an existing content type, respect field permissions, implement proper caching strategies, and follow your organization’s coding standards.

Without proper context, an AI assistant might generate generic PHP code that looks correct but:

• Uses outdated Drupal APIs
• Ignores your site’s specific entity structure
• Violates your custom coding conventions
• Fails to account for contributed modules you’re using
• Doesn’t align with your architecture decisions

Context isn’t just helpful—it’s essential for generating useful, production-ready code.

Traditional approaches to providing context have significant limitations:

• Copy-paste context: Manually including relevant files, configurations, and documentation in prompts is time-consuming and error-prone
• Static knowledge: AI models trained on general data lack specific knowledge about your codebase
• Tool fragmentation: Each AI tool has its own way of accessing external information, creating inconsistent workflows

MCP addresses these limitations by establishing a standardized protocol for context access.

How MCP Works: A Technical Overview

MCP operates on a client-server architecture where:

MCP Servers expose resources, prompts, and tools specific to a domain or system. For example, a Drupal MCP server might expose:

• Your site’s entity schemas and field configurations
• Available hooks and APIs in your Drupal version
• Custom modules and their interfaces
• Configuration management structures
• Database schema information

MCP Clients (like Claude Desktop, IDEs, or other AI tools) connect to these servers and use the exposed capabilities to provide context-aware assistance.

The protocol uses JSON-RPC 2.0 for communication, making it language-agnostic and easy to implement across different technology stacks.

A Simple Example

When you ask an AI assistant “Create a custom field formatter for product pricing,” an MCP-enabled workflow might:

1. Retrieve Resources: The AI accesses your site’s field configuration to understand existing field types and formatters
2. Use Prompts: It applies Drupal-specific patterns for field formatter implementation
3. Invoke Tools: It generates the code structure, potentially creating the necessary files in your module directory
4. Verify Context: It ensures compatibility with your Drupal version and installed modules

All of this happens automatically, without you manually providing context.

MCP and Drupal: A Natural Fit

Drupal’s complexity makes it an ideal candidate for MCP integration. Here’s why context matters so much in Drupal development:

1. API Evolution and Version-Specific Code

Drupal APIs evolve significantly between major versions. Code that’s correct for Drupal 9 might use deprecated patterns in Drupal 10. An MCP server can provide version-specific context, ensuring AI-generated code uses current best practices.

Example: Entity query syntax, dependency injection patterns, and hook implementations have all evolved. With MCP, the AI knows exactly which patterns to use for your Drupal version.

2. Site-Specific Architecture

Every Drupal site has unique characteristics:

• Custom content types and field structures
• Specific contributed module combinations
• Custom base themes and component libraries
• Organizational coding standards

An MCP server can expose this site-specific context, enabling AI to generate code that integrates seamlessly with your existing architecture.

Example: When creating a view mode, the AI can reference your actual content types, fields, and display configurations rather than generating generic examples.

3. Configuration Management Complexity

Drupal’s configuration system is powerful but intricate. MCP can provide:

• Schema definitions for configuration entities
• Dependencies between configurations
• Override hierarchies and environment-specific configs
• Validation rules for configuration values

This enables AI to help manage configurations correctly, respecting dependencies and maintaining consistency across environments.

4. Module Development Patterns

Drupal custom module development involves numerous patterns:

• Hook implementations
• Plugin systems (blocks, fields, formatters, filters)
• Service definitions and dependency injection
• Event subscribers and dispatchers
• Form API structures

An MCP server documenting these patterns helps AI generate properly structured modules that follow Drupal conventions.

5. Testing and Quality Assurance

Drupal testing involves PHPUnit, Kernel tests, Functional tests, and JavaScript tests. MCP can provide:

• Test base classes appropriate for your use case
• Mock data structures matching your entities
• Coverage requirements and testing standards
• Integration with your CI/CD pipeline

Real-World Benefits for Drupal Development

Integrating MCP into your Drupal workflow offers concrete advantages:

Faster Onboarding

New team members can leverage AI with MCP to understand site architecture, find relevant code examples, and learn project-specific patterns faster.

Consistent Code Quality

By encoding your coding standards and architectural decisions in MCP resources and prompts, you ensure AI-generated code aligns with your quality expectations.

Reduced Context Switching

Instead of constantly referencing documentation, searching through code, or checking API references, developers can ask questions and get contextually accurate answers inline.

Better Documentation

MCP encourages documenting your system’s structure and patterns in machine-readable formats, which benefits both AI and human developers.

Safer Refactoring

When refactoring legacy code, AI with MCP can understand the current implementation, dependencies, and test coverage, suggesting safer transformation approaches.

Implementing MCP for Your Drupal Site

While MCP is relatively new, implementing an MCP server for Drupal could involve:

1. Exposing Schema Information: Making entity definitions, field configurations, and view modes available as MCP resources
2. Providing Code Patterns: Creating prompt templates for common Drupal development tasks
3. Enabling Safe Actions: Implementing tools for scaffolding modules, creating configurations, or running drush commands
4. Integrating Documentation: Connecting your project’s documentation, coding standards, and architectural decision records

The protocol’s flexibility means you can start small—perhaps exposing just your custom module structure—and expand as you see value.

The Broader Context: AI-Assisted Development

MCP represents a maturation of AI-assisted development. We’re moving from:

• “AI as code generator” → “AI as context-aware development partner”
• “Generic suggestions” → “System-specific recommendations”
• “Copy-paste workflows” → “Integrated assistance”

For Drupal developers, this evolution is particularly significant. Drupal’s power comes from its flexibility and extensive API surface, but that same complexity can make development challenging. MCP bridges that gap by giving AI the context it needs to be truly helpful.

Looking Forward

As MCP adoption grows, we can expect:

• Framework-specific MCP servers: Pre-built servers for Drupal, WordPress, Laravel, and other platforms
• IDE integration: Deeper integration with development environments for seamless context access
• Collaborative context: Teams sharing MCP configurations to maintain consistent AI assistance across developers
• Quality improvements: AI suggestions that better understand nuanced requirements and system constraints

Getting Started

While MCP is still emerging, you can prepare by:

1. Document your architecture: Create clear documentation of your Drupal site’s structure, patterns, and conventions
2. Standardize patterns: Identify common development patterns and document them consistently
3. Experiment with context: Practice providing rich context to AI tools and observe the quality improvements
4. Monitor MCP development: Watch for Drupal-specific MCP implementations and contribute where possible

Conclusion

Model Context Protocol represents a fundamental advancement in how AI assistants interact with complex systems. For Drupal developers, it offers a path toward AI assistance that truly understands the intricacies of our platform—from entity systems to configuration management to version-specific APIs.

Context isn’t just a nice-to-have; it’s the foundation of useful AI assistance. MCP provides the structure to deliver that context reliably, opening new possibilities for productive, context-aware development workflows.

As the Drupal community explores MCP integration, we have an opportunity to define how AI can best support our unique development needs. The protocol’s open nature means we can build solutions that respect Drupal’s complexity while making that complexity more navigable.

The future of Drupal development isn’t about AI replacing developers—it’s about AI understanding Drupal well enough to be a truly helpful partner. MCP makes that future possible.

Want to explore how AI tools can enhance your Drupal development workflow? Contact us to discuss strategies for integrating modern development practices into your projects.