The Strategic Case for Building MCP Servers

What MCP is (and isn’t)

MCP is a protocol specification, not a software library. Similar to REST, developers adopt MCP by authoring servers that implement its rules and interfaces. These servers manage the interactions, capabilities, and context exchanges defined by the protocol, allowing AI systems to act as clients.

In other words:

• ✅ MCP is a standard for developers to implement.
• ✅ We adopt MCP by authoring servers that support the protocol.
• ❌ MCP is not software we install or a library we add to our applications.

While REST is resource-centric, MCP adds context-centric and agent-centric capabilities designed for AI interaction. When authoring MCP servers, you focus on why AI models need information, what they can see, and which actions they are allowed to trigger. This distinction is important because MCP orchestrates AI behaviors systematically, providing structured and predictable communication, unlike free-form prompt interactions.

Why now: core challenges of building servers for AI

The emergence of MCP is a direct response to the pain points in modern AI development. If you build servers for AI systems, you likely face these challenges. More importantly, you’ll encounter specific architectural decisions around each of these areas when implementing MCP servers:

• Integration Fragmentation: Major AI frameworks require different server interfaces, forcing developers to create multiple implementations of the same functionality. → This drives integration architecture decisions about abstraction layers, code reuse patterns, and platform compatibility strategies.
• Context Delivery Chaos: AI systems often request irrelevant data or misuse server capabilities, leading to poor performance and wasted resources. → This requires context optimization decisions about data filtering, token management, and information scoping strategies.
• Tool Interface Drift: Minor API changes can break dependent AI systems, creating brittle and hard-to-maintain integrations. → This necessitates API design decisions about versioning strategies, backward compatibility, and interface stability patterns.
• Security and Compliance Gaps: The lack of standardized consent and audit mechanisms in custom server implementations creates significant compliance risks. → This demands security boundary decisions about access control, audit logging, and governance framework implementation.
• Deployment Complexity: Managing server implementations across different AI environments requires custom integration work for each platform, increasing maintenance overhead. → This involves deployment architecture decisions about packaging, distribution, configuration management, and environment consistency.

Why a protocol: the case for standardization

Standardized server interfaces are crucial for AI infrastructure. AI models, as probabilistic agents, need structured, deterministic interactions with external systems. Without a protocol like MCP, developers face a cycle of building custom, fragile integrations for each AI platform, leading to duplicated effort and maintenance challenges.

This ad-hoc approach often results in:

• Platform-specific server implementations, leading to code duplication.
• Custom authentication patterns for each AI platform.
• Fragile API versioning that breaks integrations.
• Inconsistent capability discovery by AI systems.

While this may work for small projects, it is unsustainable for multi-vendor AI applications that require security reviews and reproducible audits. A protocol like MCP offers several advantages:

• Cross-Platform Compatibility: A single MCP server can work with any compliant AI system, creating a plug-and-play ecosystem.
• Composable Server Architecture: Server capabilities can be chained with other MCP servers, allowing for complex, modular workflows.
• Predictable Server Behavior: Every action is typed, logged, and replayable, which is crucial for debugging, auditing, and ensuring consistency.
• Built-in Governance: Consent, capability negotiation, and provenance are designed into the protocol, simplifying compliance and enhancing trust.

The MCP client-server interaction model

MCP defines a client-server model where you author MCP servers that expose capabilities to MCP hosts (applications that integrate servers, sometimes but not always AI/LLM apps).

The 6-step interaction flow

• Server Advertises Capabilities. Servers expose Tools (functions), Resources (data sources), Prompts (templates), and optionally Roots (filesystem boundaries) through discovery.

• Host Makes Capabilities Available. The Host (user-facing app) creates Clients to communicate with Servers, queries capabilities, and makes them available to the model or user. Prompts are explicitly user-selected.

• Model Processes Context. The model reasons over user input plus available Resources, Prompts, and Tool descriptions to decide what might be useful.

• Model Requests Tool Execution (Optional). The model may emit a Tool call with parameters. Many interactions use only Resources for context.

• Host Validates and Forwards. The Host intercepts all Tool calls, validating against policies, permissions, and user consent:

6. Server Executes and Responds. The Server executes deterministically and returns results via the Client–Host connection. For Resources-only interactions, the Host passes data directly to the model.

This model creates a clear separation of concerns. The protocol is transport-agnostic, and higher-level concerns like authentication and pagination are handled outside the direct MCP interaction, maintaining guardrails around what models can see and do.

Note on Server Capabilities:

MCP servers expose three types of capabilities with different interaction patterns:

• Tools: Model-controlled functions that the AI can execute automatically
• Resources: Contextual data that the AI can access and use directly
• Prompts: User-controlled templates that users explicitly select to guide interactions

Host vs. client: key distinction

The Host (User-Facing Application):

• The app users interact with (IDE, notebook, chat app, etc.)
• Creates and manages multiple Clients
• Enforces security, permissions, and consent
• Integrates Server results into the user experience

The Client (Protocol Participant):

• Can be a human user or an agent acting through the Host
• Maintains a 1:1 stateful session with a Server
• Translates requests into protocol messages
• Handles negotiation, routing, and error handling
• Provides isolation so Servers stay separated

Key design principles

1. Servers should be simple to implement
   • Complexity lives in the Host.
   • Servers expose narrow, well-defined capabilities.
2. Servers should be composable
   • Each Server is focused and isolated.
   • Hosts can combine them seamlessly via the shared protocol.
3. Servers are isolated by design
   • Servers never see the whole conversation.
   • They only receive minimal context needed.
4. Features evolve progressively
   • The protocol defines a minimal core.
   • Extra features are opt-in and negotiated.
   • Backwards compatibility is preserved.

MCP’s core components

MCP defines three primary server-side primitives and three client-side features that work together to enable sophisticated AI interactions.

Server features

Client features

Why developers should adopt MCP now

1. Ecosystem network effects. Early adoption positions developers to benefit from network effects as more AI applications adopt MCP, providing automatic access to an expanding ecosystem.

2. Implementation simplicity. MCP’s standardized approach reduces complexity, allowing developers to focus on core functionality rather than learning multiple platform-specific integration patterns.

3. Future-proof architecture. Building on an open protocol standard protects investments from platform changes and proprietary lock-in while maintaining compatibility as the ecosystem evolves.

4. Enhanced capability expression. The protocol’s rich primitive model allows developers to express complex capabilities in ways AI systems can understand and utilize effectively.

Common misconceptions about MCP servers

”MCP server replaces my existing APIs”

Reality: MCP servers complement existing APIs by providing a standardized interface layer. Your core business logic remains unchanged.

Best Practice: Think of MCP as a translation layer that makes existing capabilities accessible to AI systems.

”MCP always requires human approval for actions”

Reality: MCP supports both interactive (human-supervised) and autonomous (policy-driven) operation modes, allowing for flexible deployment scenarios.

Best Practice: Design servers to work in both modes by clearly documenting which operations require human oversight versus those that can operate autonomously under pre-configured policies. Include this guidance in your tool descriptions, API documentation, and server capability declarations so MCP hosts can configure appropriate authorization policies for interactive versus automated deployment scenarios.

”MCP is only for simple tools”

Reality: MCP supports sophisticated capabilities through its rich primitive model, including agentic behaviors and dynamic user interaction.

Best Practice: Start with simple tools to learn patterns, then gradually expose more complex capabilities.

”I need to rebuild everything for MCP”

Reality: MCP servers typically wrap existing functionality with standardized interfaces. Most work involves mapping existing capabilities to MCP primitives.

Best Practice: Identify which existing capabilities map to MCP’s resource, tool, and prompt primitives, then create thin wrapper layers.

”MCP locks me into Anthropic’s ecosystem”

Reality: MCP is an open protocol supported by multiple organizations. Developers maintain full control over implementations.

Best Practice: Design servers to be transport-agnostic and avoid dependencies on specific AI system features for maximum compatibility.

”My server should handle everything”

Reality: Monolithic servers reduce composability and create maintenance challenges. Author focused servers around specific domains.

Best Practice: Focus on doing one thing well rather than trying to be everything to everyone. Let AI systems orchestrate across multiple servers.

What’s Next: From Strategy to Architecture

This article covered the strategic foundations of MCP: what it is, why it matters, and how it works. But strategy alone doesn’t ship robust servers. The next article moves into the architectural decisions that define real implementations.

You’ll face questions like: How do you optimize context without overwhelming AI? Design integrations that work across platforms? Structure multi-step workflows while preserving security and governance? These are the concrete challenges that decide whether your MCP server becomes a strategic asset or an abandoned project.