Model Context Protocol (MCP) is an open standard that defines how applications provide context to large language models (LLMs). Similar to how a USB port offers a universal way to connect devices to peripherals, MCP provides a consistent method for connecting AI models to live data sources, APIs, and application tools. This makes it possible to build agents and complex workflows on top of LLMs that can work with real-time information and perform specific actions.
What is MCP?
MCP changes the role of AI models from static knowledge sources into active, adaptable components of your workflows. By giving models secure, structured access to the right data and the ability to take defined actions, MCP ensures their output is relevant, up to date, and aligned with your specific needs. Instead of producing answers in isolation, AI can now interact with your systems, integrate with existing processes, and deliver results that fit directly into your work.
The Background
Before Model Context Protocol, connecting AI models to applications was a messy and expensive challenge. Developers had to build new integrations for every single connection between an AI model and a data source. While some platforms offered proprietary stop-gaps, these solutions only locked developers into a single vendor's ecosystem. Recognizing the need for a universal standard, Anthropic introduced MCP in late 2024. The protocol provides a robust, open blueprint for AI integration, offering a way to build one connection that works across the entire AI landscape.
The Monogram Coding Standards MCP
For our team, MCP presented a clear opportunity to enforce our high standards for code quality directly within our code editors. To achieve this, we built the Monogram Coding Standards MCP server. Our developers can now add a simple phrase to their prompts, like "...using Monogram standards", and our server provides the necessary context. It does this in two primary ways: first, by acting as a reference, directly answering questions about our standards. Second, and more powerfully, by automatically bundling our best practices with any code generation request. This transforms our internal standards from a static document into an active part of our workflow, ensuring the code we produce is consistent and high-quality from the very first draft.
Leveraging Platform-Specific MCP Servers
Monogram’s MCP server is designed to uphold the company’s high standards for code quality. To achieve deep platform-specific knowledge, we use it in conjunction with official servers from the platforms themselves. For example, on a Shopify project, the official MCP server for the Shopify Dev Assistant makes their latest developer docs and API schemas available to any compatible AI assistant our team uses. For a headless site using Contentful, their official MCP server connects directly to the Contentful Management API, allowing our developers to create, update, and manage entries, assets, and content models through natural language. For example, a developer could ask an AI assistant to add a new field to a content type, upload and link media assets, or update multiple entries at once. This combination ensures the final result reflects both deep platform expertise and our own signature quality standards.
Driving Quality at Monogram with MCP
By combining platform-specific MCP servers with the Monogram Coding Standards MCP server, we ensure our team has universal access to both our internal best practices and up-to-date platform information, independent of the AI model or code editor they use. This approach has been transformative for our workflow, allowing us to maintain a consistent and high-quality codebase across all projects. For us, it’s about quality, precision, and consistency, not just speed.