What is MCP and Why Should You Care?

The Model Context Protocol (MCP) is an emerging industry standard developed by Anthropic, the company behind Claude, and others that makes AI systems more modular, interpretable, and workflow-aware. Instead of starting from scratch with each query, MCP enables persistent memory and defines instructions across different tools and systems.

As Anthropic explains:

"MCP is an open protocol that allows developers to define reusable, modular components of context—like documents, files, or APIs—and share them with AI models in a consistent, interpretable way."
Source: Anthropic

In simpler terms, MCP creates a shared language between tools, content, and AI models, so AI can understand your business environment instead of acting like a one-off assistant.

The result? AI workflows that are more useful, repeatable, and scalable across departments.

Why Should You Pay Attention?

Time is your most valuable resource.

Too many teams and industry experts are still stuck doing manual processes like:

• Recapping meeting notes manually

• Searching through inboxes for context

• Entering repetitive data into CRM systems

• Rewriting the same client deliverables again and again
  

These tasks are ripe for AI integration, not in a "replace people" way, but in a "free them up to do smarter work" way. You want to free up your staff's intellectual capital and make room for AI capabilities.

Where to Start: Practical AI Use Cases for Firms

According to Simple.AI, MCP enables firms to "go further" by eliminating friction between departments and making AI more reliable across a knowledge ecosystem

You don't need to implement AI everywhere overnight. Start with one high-friction area per team. Some common wins include:

Sales & Client Services

• Automatically log and summarize meeting notes

• Update CRM records with generative AI context

• Enrich leads with AI-assisted research

Marketing

• Improve social media and blog content in minutes

• Analyze campaign performance with predictive models

• Create segmentation lists based on behavior

Finance & Operations

• Extract structured data from PDFs

• Match invoices and flag duplicates

• Automate standard reports

Implementing MCP or similar structured protocols makes these AI systems more effective because they don't just work with one-off data; they learn from the whole system.

Validate Ideas Before You Scale

Before investing in any AI project, it's critical to assess:

• Is your data clean and accessible?

• Will the outcome save time, money, or reduce risk?

• Can you prototype quickly before scaling up?

At DB Services, we guide our clients through these checkpoints. It's how we ensure AI isn't just another tool; it becomes a real business advantage.

Train, Measure, and Improve

AI only works if your team uses it. That's why we support:

• Role-based onboarding

• Clear KPIs for time savings and output

• Ongoing feedback loops to refine workflows
  

Whether you're deploying AI assistants, building internal copilots, or enabling cross-tool integrations, the goal is to reduce friction and multiply value.

Coming Soon: AI for Nonprofits

We're working on a follow-up article specifically for nonprofit organizations. It will focus on how lean teams can use AI for fundraising, grant management, and mission delivery, and how MCP can help nonprofit systems communicate more effectively. Stay tuned!

Ready to Explore AI with MCP?

The question isn't if AI will impact your firm. It's how well-prepared you are to use it intentionally.

MCP implementation is one of the most significant steps to making AI more useful across real-world workflows. If you're exploring how to get started—or how to go further—we'd love to help.

Book a free strategy session with DB Services to explore how MCP and AI can transform the way you work.