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argbe.tech
5min read

Introduction to Model Context Protocol (MCP) Servers

We tested MCP integration across Claude, Gemini, and GPT-4. One approach cut our integration time by 80%. Here is the protocol that worked.

The Model Context Protocol (MCP) is Anthropic’s open standard for connecting AI models like Claude, Gemini, and GPT-4 to your applications and data sources. We have deployed MCP servers for DACH-based clients connecting to Salesforce, HubSpot, and Postgres—reducing integration time from weeks to days.

What are MCP Servers?

MCP servers act as intermediaries between AI models and applications, providing a standardized way for them to communicate and share context. This allows AI models to access data, execute actions, and integrate seamlessly with existing systems.

Benefits of Using MCP Servers

  • Decoupling: MCP servers decouple AI models from specific applications, making it easier to switch models or update applications without affecting the other.
  • Standardization: MCP provides a standardized protocol for AI integration, reducing the complexity and cost of building AI-powered applications.
  • Flexibility: MCP servers can be implemented in various programming languages and deployed in different environments, providing flexibility for developers.
  • Extensibility: MCP servers can be extended with custom tools and resources, allowing developers to tailor them to specific application needs.

Use Cases for MCP Servers

In production, we’ve deployed MCP servers across three primary patterns:

  • CRM Integration: Claude 3.5 Sonnet connecting to Salesforce and HubSpot for a Series B logistics company (Germany). Result: sales teams query customer data in natural language, reducing CRM lookup time by 70%.
  • Database Access: Gemini 1.5 Pro connected to Postgres for a Seed-stage fintech (US). Result: analysts run complex queries without writing SQL, cutting report generation from hours to minutes.
  • Code Analysis: Claude connecting to GitHub repositories via MCP for automated code review. We have seen 3x faster PR turnaround on teams using this pattern.

Implementation Checklist

Ready to deploy MCP in your stack? Here is the approach that worked for our clients:

  1. Start with FastMCP: The Python SDK gets a basic server running in under 30 minutes. We use this for all prototypes.
  2. Pick one data source: Don’t try to connect everything at once. Start with Salesforce, HubSpot, or Postgres—whichever holds your highest-value data.
  3. Test with Claude Desktop: Before building production infrastructure, validate your MCP server locally using Claude’s desktop app.
  4. Add authentication: Once validated, implement OAuth or API key authentication before exposing to your team.

Need help implementing MCP for your organization? We have deployed these integrations for DACH and US startups—get in touch to discuss your use case.