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What is MCP?
MCP (Model Context Protocol) is an open standard introduced by Anthropic in late 2024. It defines how AI models access external resources, tools, and data sources in a structured way. The goal: a unified protocol instead of individual integrations.
Core components: MCP client (AI application that accesses external data), MCP Server (provides resources and tools), Resources (data and contexts that are made available), Tools (actions the model can perform), Prompts (reusable prompt templates).
Key benefits: Standardization (one protocol for many integrations), security (clear authorization models for data access), modularity (servers are reused across AI applications), ecosystem (growing number of ready-made servers for standard systems such as Git, Slack, and databases).
For small and medium-sized businesses, MCP is the pragmatic way to integrate AI into existing IT infrastructure. Instead of writing custom APIs for each application, companies use MCP servers—built once, reusable everywhere. This reduces integration costs and promotes standardization.
Best Practices for MCP
These six principles have proven effective:
- Security from the Start: Clearly define permissions and the access model; don’t leave them open-ended.
- Start small: Begin with a simple server for a single use case—then scale up.
- Clearly define tools: Specify precise parameters and return values—otherwise, AI will use them incorrectly.
- Require approval for critical actions: Don’t automate everything—explicitly confirm sensitive actions.
- Build in monitoring: What is being called, and how often? Errors? Abuse?
- Use off-the-shelf servers: Don’t build your own for standard systems—use existing ones.
Common Mistakes with MCP
We frequently encounter these pitfalls:
- Permissions that are too open: AI can access everything—a security risk and a compliance issue.
- Unclear tools: Poor documentation leads to incorrect calls—the application appears buggy.
- No approval logic: Critical actions without confirmation—potential for damage.
- Too many servers: Proliferation of MCP servers — governance chaos.
- No monitoring: No one knows what the AI is actually doing—flying blind.
MCP vs. Function Calling vs. Plugins
A comparison of three approaches to using AI tools:
- Function Calling: Model-specific feature (OpenAI, Anthropic) for individual tool calls.
- Plugins: Vendor-specific extensions — difficult to port between providers.
- MCP: Open standard—works across providers. Growing ecosystem.
MCP Integration with prodot
In a free initial consultation, we’ll review your AI landscape and identify MCP opportunities—for faster integration and lower costs.
As an AI partner for small and medium-sized businesses, we build MCP integrations that are secure and practical—using off-the-shelf servers, building custom servers, and ensuring governance.
What We Offer
- AI Consulting — Architecture with MCP.
- AI Agent in the Glossary — MCP is the central tool layer for agents.
- Function Calling in the Glossary — the foundation of MCP.
- Claude Code in the Glossary — MCP in action for developers.
