How to Actually Implement MCP for Your Service Company

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In our previous post, we covered what MCP might look like in a service-based organization. Now it’s time to get practical with a step-by-step checklist for putting it into action.

Alright, so MCP sounds great in theory. But how do you actually do this? Let’s walk through the steps, from easiest to most advanced. The good news: you can start today. Unlike most “transformative technology,” you don’t need to hire a dev team or wait six months for implementation. If you’re already using Claude.ai, you can start experimenting with MCP connections in under an hour.

Go to your Claude.ai settings and look for “Integrations” or “Connections.” Anthropic is actively building official MCP connectors for popular tools. As of now, you might see options for Slack, Google Drive, GitHub, or Notion. 

These are one-click connects, so all you have to do is authorize Claude to access your account, set permissions, and you’re live.

Don’t try to automate your entire business on day one. Pick one annoying, repetitive task:

• • “Search our Google Drive for all client proposals from Q4”
  • “Find the Slack conversation where we discussed pricing with Acme Corp”
  • “Show me all issues labeled ‘bug’ in our GitHub repo”

Get comfortable with how Claude uses these connections. Watch what it can and can’t do. Learn to phrase requests effectively.

Once you’re comfortable, start chaining tools together:

• • “Find the proposal in Drive, then post it to the #sales Slack channel”
  • “Search our Notion docs for the onboarding checklist, then tell me what we’re missing”

You’re building muscle memory for what’s possible.

Time investment: 1-2 hours setup, then ongoing experimentation.
Cost: Just your existing Claude subscription.
Risk: Minimal. You control permissions and can revoke access anytime.

If you want to connect tools that don’t have official MCP servers yet (like HubSpot, Asana, Salesforce), you’ll need to build or configure MCP servers yourself. This path is best for companies with technical resources, custom tool integration, and full control. 

An MCP server is essentially a small application that:

1. 1. Connects to your business tool’s API (like HubSpot’s API)
   2. Exposes standardized “tools” that Claude can call
   3. Handles authentication and data formatting

Think of it as a translator that speaks “Claude language” on one side and “HubSpot language” on the other.

The MCP community is growing fast. Before building from scratch, search for existing servers:

• • Check the official MCP repository: github.com/modelcontextprotocol
  • Look for community-built servers on GitHub
  • Many are open source and ready to use

For example, someone may have already built a HubSpot MCP server you can deploy.

Let’s say you want to connect to HubSpot. Here’s the simplified process:

1. Get your HubSpot API credentials

• • Generate a private app in HubSpot settings
  • Copy your API key

2. Set up the MCP server

• • Clone the MCP server template or existing HubSpot server
  • Configure it with your API credentials
  • Define which tools you want to expose (search deals, update contacts, etc.)

3. Host it securely

• • Deploy to a cloud service (AWS, Google Cloud, or even your own servers)
  • Ensure it’s behind proper authentication
  • Set up HTTPS for secure communication

4. Connect Claude to your server

• • In Claude’s settings, add your custom MCP server URL
  • Configure authentication tokens
  • Test the connection

Time investment: 8-20 hours for first server (depending on technical comfort)
Cost: Cloud hosting fees (typically $10-50/month)
Skills needed: Basic familiarity with APIs, deployment, and command-line tools

Once you have one server working, the pattern becomes repeatable. Add servers for:

• • Asana (project management)
  • Gmail (email integration)
  • Your internal database
  • Custom reporting tools

Each server gives Claude new capabilities.

This approach is best suited for larger organizations with compliance requirements and advanced security needs.

Before connecting business-critical tools:

• • Audit what data Claude will access
  • Review data retention policies
  • Ensure compliance with GDPR, SOC 2, or industry regulations
  • Set up proper access controls and permissions

Rather than connecting Claude directly to production systems:

• • Create a dedicated API gateway for MCP servers
  • Implement rate limiting and usage monitoring
  • Set up audit logging for all AI-initiated actions
  • Build data sanitization layers (remove sensitive info before it reaches Claude)

• • Start with a pilot team of 5-10 users
  • Monitor usage patterns and surface issues
  • Gather feedback on what’s working and what’s not
  • Expand access once you’ve validated the approach

Your team needs to know:

• • What tools Claude can access
  • How to phrase requests effectively
  • What’s possible vs. what requires human intervention
  • When to use MCP vs. when to do things manually

Time investment: 2-3 months for full rollout
Cost: Significant (internal dev time, infrastructure, training)
Benefit: Enterprise-grade security, full control, customized to your workflow

The mistake: “Let’s connect every tool and automate everything immediately!”

The fix: Start with information retrieval, not actions. Get comfortable having Claude read data before you let it write data. Search before you create. View before you update.

The mistake: Giving Claude full admin access to all systems.

The fix: Use role-based access. Claude should only have permissions your team members would have. If an account manager can’t delete deals in HubSpot, neither should Claude.

The mistake: Letting AI automatically send client emails or update contracts without review.

The fix: Build in confirmation steps for high-stakes actions. Claude can draft the email, but a human should review before sending. It can suggest deal stage changes, but someone should approve.

The mistake: Your CRM has duplicate contacts and outdated information. Claude will surface that messiness.

The fix: MCP implementation often reveals data hygiene issues. Use this as an opportunity to clean up your systems. Better data = better AI results.

Let’s be honest about investment:

Time costs:

• • No-code start: 5-10 hours total (setup + learning)
  • DIY developer: 40-100 hours for first full implementation
  • Mid-Sized Company: 200-400 hours for first full implementation
  • Enterprise: 400+ hours (planning, building, rolling out)

Money costs:

• • No-code: $0 beyond your Claude subscription
  • DIY: $20-100/month for hosting + your dev time
  • Enterprise: Varies wildly based on scale and requirements

Opportunity costs:

• • The first month will have a learning curve
  • Your team needs time to adapt to new workflows
  • You’ll discover inefficiencies in current processes (which is actually good, but uncomfortable)

Before you start implementing, ask: “What’s the one task that wastes the most time for my team each week?” Is it compiling status reports? Searching for client information? Coordinating project kickoffs? Tracking down who’s responsible for what? Start there. Build your MCP implementation around solving that specific pain point. Prove the value. Then expand.

Implementing MCP isn’t an all-or-nothing proposition. You can start with baby steps, connecting one or two tools and seeing what’s possible, then gradually build a more sophisticated setup as you prove value.

The barrier to entry is lower than you think. The no-code path gets you experimenting today. The DIY path gives you full control without massive investment. The enterprise path scales securely when you’re ready.

Most importantly: you don’t need to figure this all out before you start. The best way to understand what MCP can do for your business is to just connect something and start asking questions. You’ll be surprised how quickly “this is interesting” becomes “how did we live without this?”