5 beste praktijken voor het gebruik van MCP servers

Door Maneeza Malik, Product Marketing Directeur

De Modelcontextprotocol (MCP) has emerged as an open standard for connecting AI applications and agents with enterprise tools, data and systems. Introduced by Anthropic in November 2024, MCP provides a common interface for AI agents and applications to interact with:

• Tools: functions an AI agent can invoke, such as querying a database, creating a support ticket or retrieving customer information
• Resources: data and context an AI agent can access, such as files, database records or enterprise documentation
• Prompts: reusable instructions or templates that guide how an AI agent interacts with these capabilities

What Is an MCP Server?

An MCP server exposes tools, resources and prompts through the MCP interface. Instead of building separate integrations for each AI application or agent, organizations can expose shared capabilities through a common interface that multiple AI agents can discover and reuse.

MCP Doesn’t Replace APIs

MCP and APIs serve different purposes. APIs expose application and system capabilities, while MCP provides a standardized way for AI applications and agents to discover and interact with tools, resources and prompts that may be backed by those APIs and systems. An MCP server can expose these capabilities through existing APIs and enterprise systems rather than replacing them.

Know When You Need MCP

MCP isn’t automatically the right choice for every AI application. Direct API integrations may be simpler when you have a small number of AI agents with stable, application-specific workflows.

MCP becomes more valuable when capabilities need to be shared, reused, consistently accessed and governed across AI agents, teams or applications.

The key question isn’t whether to use MCP. It’s where MCP can add value by reducing integration complexity, increasing reuse and providing a stronger foundation for AI applications as they scale.

The following five best practices provide a framework for making that decision and using MCP effectively when and where it makes sense.

Best Practice #1: Start With the Integration Problem, Not the Protocol

Don’t start with, “Where can we use MCP?” Instead, start with, “What integration problems are we trying to solve?”

If all you have is one AI agent or a handful of AI agents that need access to a few stable APIs and resources that aren’t shared, then a direct integration may be all you need. In that case, MCP may not provide enough additional value to justify the added abstraction.

MCP becomes more compelling when the same capabilities need to be consumed by multiple AI agents, teams or AI applications. Instead of repeatedly building and maintaining the same agent-facing integrations, an MCP server can expose those capabilities through a common interface.
When considering MCP, look for problems such as:

• The same enterprise data or capabilities need to be accessed by multiple AI agents
• Teams are repeatedly building similar integrations
• AI applications need consistent access to shared capabilities
• Integration complexity is increasing as the number of AI agents and applications grows
• You want to standardize how tools and capabilities are exposed to AI clients

The goal isn’t to use MCP for its own sake. The goal is to reduce integration complexity, standardize access to capabilities, and create reusable building blocks that can be shared across AI agents and applications.

Best Practice #2: Identify Capabilities Worth Sharing

Once you’ve identified a shared integration problem, look for the capabilities multiple AI agents need.
Examples might include:

• Searching customer records
• Checking inventory
• Creating a customer support ticket
• Querying financial data
• Searching internal knowledge

The strongest candidates are capabilities that are broadly useful, relatively stable and likely to be reused across multiple AI agents and applications.Centralizing these capabilities behind an MCP server can eliminate duplicated integrations and give AI agents a consistent way to access them.

This is where MCP begins to function as shared infrastructure for AI rather than simply another integration layer.

Best Practice #3: Design MCP Servers for Agents, Not APIs

An MCP server should do more than simply mirror existing API endpoints through a standardized interface. Design its capabilities around what AI agents need to accomplish, rather than simply mirroring the endpoints of the underlying APIs.

For example, instead of exposing dozens of low-level endpoints, provide purposeful tools such as:

• search_customer
• get_open_orders
• create_support_case

Purposeful tools make it easier for AI agents to discover, understand and compose capabilities into larger workflows.

The goal is to give AI agents capabilities they can reliably discover, understand, select and use — not simply to reproduce an existing API surface.

Best Practice #4: Treat MCP Servers as Shared Infrastructure

An MCP server may start as a simple way for one AI agent to access a few tools or data sources. But once multiple agents, applications or teams depend on it, the server effectively becomes shared infrastructure. A change to a tool, permission, schema or backend behavior can affect every consumer.

That means MCP servers should be designed and operated with the same discipline applied to other shared services and APIs: clear contracts, predictable behavior, security controls, observability, testing, versioning and governance should be treated as first-class concerns.

Pay close attention to:

• Clear tool names: Use names that tell an AI agent what the tool actually does.
  • For example: “search_customer rather than execute_function_7”.
• Precise descriptions: Describe what the tool does, what information it expects and when it should be used.
  • For example: “find customers by email, account ID or company name”.
• Well-defined inputs and outputs: Use explicit schemas and predictable response structures.
  • For example: “customer_id: string → structured customer object”.
• Appropriate abstraction levels: Expose meaningful business capabilities rather than leaking implementation details.
  • For example: provide “create_support_case instead of exposing every underlying API call required to create one”.
• Sensible boundaries: Clearly distinguish between operations that read information and operations that create, modify or delete data.
  • For example: destructive or consequential actions should have appropriately explicit interfaces and controls.
• Strong evaluation and testing: Test not only whether a tool works, but whether agents select the correct tool, provide valid inputs, interpret outputs correctly, and respect authorization and safety constraints. Test failure modes and edge cases as well as successful execution.
• Security and permissions: Apply least-privilege access and ensure that AI agents can perform only the operations they are authorized to perform. Exposing a tool through MCP should not bypass or weaken authorization controls enforced by the underlying systems.
• Reliability and observability: Monitor tool failures, latency, dependency failures and unusual usage patterns. When an MCP server is shared, one unreliable tool can become a reliability problem for many AI agents and workflows.
• Change management and versioning: Treat tool schemas, descriptions, permissions and behavioral changes as API changes. Changes that seem minor — including changes to tool names, descriptions, schemas or permissions — can affect how AI agents discover, select or invoke tools and may impact downstream workflows.

This matters because MCP servers can give AI agents access not only to information, but also to actions that can change systems and data. Once those capabilities are shared across AI agents and teams, the MCP server becomes part of the organization’s operational and security boundary. Reliability, security, compatibility and governance therefore become just as important as functionality.

Best Practice #5: Use MCP Where It Creates Leverage

MCP provides the greatest leverage when it solves a broader architecture problem, rather than simply adding another way to connect an API to an AI agent.

Use this checklist to assess where MCP makes sense.

Direct APIs may be enough when:

• You have one or a few AI agents
• Workflows are tightly defined and stable
• Integrations are application-specific
• Capabilities aren’t shared across AI agents or applications
• A single team owns the AI agents and their integrations
• Direct API integrations are simple, stable and easy to maintain

MCP becomes more compelling when:

• Multiple AI agents need the same capabilities
• Multiple teams are building and deploying AI agents
• The same integrations are being duplicated
• Capabilities need to be reused across AI applications
• AI agents need standardized discovery and access to tools
• You are building multi-agent workflows or orchestration
• Your integration architecture is becoming a shared platform problem

Think of this as a spectrum rather than a binary choice.

You don’t need to move everything behind MCP. Start with the capabilities where standardization, reuse and shared governance provide clear value.

Drive AI Integration at Scale with MCP Servers

MCP is most valuable when AI integration becomes a shared architecture problem. If multiple AI agents, teams or AI applications need consistent access to the same capabilities, an MCP server can reduce duplicated integrations and provide a standardized foundation for discovering, securing and governing those capabilities.

The best approach is to start with the problem. Identify what should be shared, design capabilities around how AI agents use it, and operate MCP servers with the reliability and governance expected of shared infrastructure.

The question isn’t “should we use MCP servers?” It’s “where will MCP create enough reuse, consistency and scalability to justify introducing it?”

Knowing the difference is what turns MCP from another integration layer into a strategic advantage for scaling AI.

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