How MCP Server eliminates operational bottlenecks for scaling companies? Case study

The 15-hour problem every scaling company faces

Picture this: It’s Monday morning, and your CMO needs to update the pricing page before a major product announcement. The change itself is simple: a headline, a new pricing tier, maybe a promotional banner. But the process? Often anything but simple.

First, there’s a ticket. Then waiting for developer capacity. Code review. Deployment pipeline. Testing on staging. Finally, production. By the time the change goes live, it’s Thursday afternoon, and the launch momentum is gone.

Meanwhile, your CEO is preparing for a board meeting. They need revenue metrics from Stripe, customer acquisition costs from Google Analytics, pipeline data from Salesforce, engineering velocity from Jira, and burn rate from your financial system. Each platform requires a login, navigation through dashboards, manual export, and then the real time sink: correlating everything into a coherent story.

Four hours later, they have a presentation.

This isn’t hypothetical. Research shows C-level executives at fast-growing digital companies spend 15-25 hours weekly on operational overhead: context-switching between tools and manually compiling information that should be instantly accessible.

We present an AI-powered solution: a case study implementing an MCP server that eliminates operational bottlenecks for scaling companies. Our implementation shows how we shortened the time to add new articles to our website to minutes. But this is just one example to show how an MCP server can simplify processes at your company.

What is MCP Server?

MCP Server (Model Context Protocol Server) is a protocol that allows AI systems like Claude to connect to your business tools and act on your behalf. Simply put: MCP is a bridge between artificial intelligence and your company’s systems.

How does it work in practice?

How MCP Server works in practice

Think of MCP Server as giving your AI assistant actual access to your company’s tools, not just knowledge about them.

The fundamental difference:

Traditional AI is like having a smart consultant who can give you advice but can’t actually do anything in your systems. Ask them about your website traffic, and they’ll explain how to find it in Google Analytics. Ask them to update your pricing page, and they’ll tell you the steps to follow.

AI with MCP Server is like having an executive assistant with login credentials to all your business tools. Ask about website traffic, and they pull the actual numbers from Analytics. Ask them to update the pricing page, and they make the change directly in your CMS.

What this looks like day-to-day:

Let’s say you’re preparing for next week’s board meeting. You need a comprehensive business update: revenue trends, customer growth, team productivity, and current sales pipeline. With MCP Server you simply tell the AI:

“Prepare my board meeting package for next week: revenue trends from last quarter, customer acquisition breakdown, engineering deliverables, current sales pipeline, and team headcount changes.”

The AI connects to all your systems, pulls current data, creates visualizations, and delivers a ready-to-review presentation. Time: 5 minutes of review instead of an hour of manual work.

Is connecting the MCP server to your tools safe?

Just like you choose which software tools your company uses, you choose which systems to connect to MCP Server. Maybe you start with your website and analytics. Later you add your CRM and project management tools. There’s no forced package; you build what serves your specific needs.

You decide what the AI can and cannot do:

- Some actions might happen automatically (pulling reports, checking status)

- Others might require your approval (publishing content, making changes)

- Sensitive operations can require two-person confirmation

Think of it like setting permissions for a new employee: you grant access based on what makes sense for your operations.

MCP doesn’t create new security risks. It uses your existing login credentials and permissions. If someone on your team doesn’t have access to financial data normally, they won’t have access through MCP either. Everything is logged, just like your current systems.

MCP server implementation in practice: updating website challenge

At Boldare, our website runs on Netlify with Decap CMS managing our blog. In practice, publishing a blog post meant logging into the CMS, manually filling metadata fields, hunting through dropdowns for categories and tags, formatting markdown, checking image links, previewing, adjusting, saving as draft, and submitting for review. The entire process took 15-30 minutes per post, just for administrative overhead, not actual writing.

The bigger problem, one that becomes critical in organizations where many people make website changes, was the potential for inconsistency. When multiple team members access a CMS, they naturally approach it differently: some fill every metadata field, others skip optional ones, formatting varies, tag usage becomes scattered. These inconsistencies accumulate over time and are hard to catch systematically. This is a challenge we frequently hear from clients with extensive digital systems that require constant updates across distributed teams.

Building the solution: MCP Server meets content management

As an AI-native company, we approached this operational friction the way we typically do: by letting AI handle it. We decided to implement an MCP Server that would allow us to add blog posts through a single prompt in an LLM, eliminating the entire CMS interface workflow.

The system we built is flexible: technically, the LLM can write articles from scratch in the same prompt that publishes them. However, at Boldare, our blog content is created by our authors and domain experts, people with real experience and unique perspectives.

The MCP Server handles the operational overhead of publishing, not the creative work of writing. This distinction matters: we’re not replacing human expertise with AI generation, we’re removing the administrative friction that gets in the way of that expertise reaching our audience.

MCP Server: Core technology stack

Key architectural decisions

One of our most critical decisions was to operate directly on the file system that mirrors our blog post structure, rather than relying solely on API calls. This might seem like a small technical detail, but it had profound implications for performance. By working with the actual files, we could implement sophisticated search algorithms that dramatically improved how well the MCP Server responded to queries.

We discovered something interesting during development: LLMs frequently use the search functionality to find inspiration or check existing content before creating new articles. They don’t just blindly generate; they look at what’s already been written, learn from the style and structure, and create something consistent with the existing body of work. This meant that fast, accurate search wasn’t just a nice-to-have feature. It was essential for the entire system to work well in practice.

Reverse-engineering the CMS workflow

Decap CMS operates through a specific Git-based pattern, but when we started this project, there was essentially no documentation explaining how it worked under the hood. We had to reverse-engineer the entire system by analyzing our existing repository, examining pull requests, and diving into the Decap CMS source code on GitHub. Through this detective work, we discovered the CMS workflow structure:

Through trial and error with test articles, we eventually achieved complete compatibility. Now articles created through our MCP Server appear seamlessly in the CMS interface and flow through our standard editorial process as if they’d been created manually. Getting this right was crucial. Without it, we’d have two parallel content systems that didn’t talk to each other.

Performance optimization

Our repository presented a genuine performance challenge. With approximately 5,700 files including hundreds of blog articles, case studies, and translations, we couldn’t afford slow operations. Initially, we faced several bottlenecks:

• Repository cloning on container startup: approximately 30 seconds - Full-text search too slow for responsive UX - Parsing frontmatter from hundreds of files created noticeable delays

We solved this through several complementary strategies:

The results exceeded our expectations. Search across our entire blog now takes under 100 milliseconds. Listing articles takes under 50 milliseconds. Creating a new article, including the git commit, takes roughly 2 seconds. These response times make the system feel instant in practice, which is critical for user adoption.

Security architecture

Security wasn’t an afterthought. We built it into the architecture from day one. The server uses API key authentication for MCP connections, enforces HTTPS through certbot-managed certificates, and connects to GitHub using proper OAuth authentication rather than storing credentials in the codebase. Everything runs in an isolated Docker container, adding another layer of protection.

Security and scalability features:

- Stateless design enables horizontal scaling \

• Health check endpoints for monitoring \
• Graceful degradation on component failure \
• Single instance currently handles all load comfortably

This resilience matters in production; systems need to degrade gracefully, not catastrophically.

What’s possible with MCP Server

We built this to solve our own operational friction. The same approach can work for data aggregation, customer insights, infrastructure operations, project coordination, or competitive intelligence – anywhere your team spends time gathering information across multiple systems.

Interested in exploring what this could look like for your organization? Get in touch and we’ll walk through your specific workflows.