Three Common Misconceptions About Docker MCP and How to Avoid Them

Many developers believe they have a firm grasp on Docker's Model Context Protocol (MCP), but common misunderstandings can undermine AI system reliability and agent design. Let’s break down the most frequent MCP misconceptions and explore how to use MCP as intended for robust, production-grade AI.

Mistake #1: Treating MCP as Just Another API

It’s easy to lump MCP in with familiar protocols like REST or gRPC, invoke a tool, get a response, move on. But MCP is fundamentally different. Designed for large language model (LLM) tool use, MCP supports intent mediation and context exchange. 

Its real value is in enabling non-deterministic agents to safely and reliably interact with deterministic APIs, creating a bridge between flexible model reasoning and dependable execution.

• Tool interfaces for models: Go further than endpoints and define preconditions, expected outcomes, and what the tool affords an agent.
  
  
• Context surfaces: Use prompts, elicitations, and resources to inform model behavior beyond simple request/response cycles.
  
  
• Seam for reliability: MCP divides non-deterministic planning from deterministic execution, enabling auditable, robust interactions.

Instead of exposing business logic directly, wrap it with MCP tools specifying guardrails and outcomes. Design for determinism and idempotency in the “last mile” to ensure reliable results. Avoid pitfalls like treating MCP as a stateful business API or enforcing strict schemas, agents need validation, retries, and clear, machine-checkable outcomes.

Mistake #2: Equating Tools with Agents

It’s a common trap to confuse tools with agents. Tools execute specific actions, while agents plan, track goals, re-plan, and measure progress. Although some LLM demos blur the distinction, true agents operate in loops, continually deciding what to do until objectives are met.

• Agency: Agents handle goal tracking, re-planning, and error recovery.
  
  
• Evaluation: Agents use explicit success criteria and fitness functions, not just status codes.
  
  
• Memory and context: Agents adapt prompts and resource usage over time.

Keep the control loop outside the tools themselves. Give agents clear success metrics, add retries and escalation paths, and use MCP’s elicitation features to involve humans when confidence is low. Don’t shoehorn planning into single tool calls or judge agent effectiveness by tool latency, agents require structured goals, constraints, and traceability for every action.

Mistake #3: Thinking MCP Is Just About Tools

MCP is more than tools passing JSON. It includes resources, prompts, and elicitations which are the key components for building context-rich, reliable AI. Early versions often ignored these features, but they’re crucial for durable agent design.

• Resources: Structured artifacts (files, tickets, etc.) that agents can read, write, and reference.
  
  
• Prompts: Versioned, reusable instruction sets for models, testable and auditable.
  
  
• Elicitations: Structured flows for human clarification when agents are uncertain.

Adopt design patterns like mapping external data into MCP resources, managing prompts with version control, and defining elicitation checkpoints for human input. Avoid using MCP as a thin voice layer over existing services or hard-coding prompts; instead, manage these elements through MCP for flexibility and auditability.

How MCP Enables Reliable AI Systems

MCP’s real strength is in providing a seam between non-deterministic planning (model reasoning, tool selection, replanning) and deterministic execution (tool runs, input validation, side-effect management). This architecture connects tools, resources, prompts, and elicitation mechanisms, ensuring observability and governance.

• Trace and audit every step, from planning to tool invocation to resource updates.
• Version prompts and tool definitions for reproducibility.
• Enforce access controls and rate limits at the MCP boundary for security and reliability.

Takeaway: Use MCP for Reliable, Trustworthy AI

Treating MCP like a traditional API leads to fragile, one-off demos. The key is to recognize the role of tools as deterministic executors, agents as strategic planners, and to leverage the full MCP toolkit (resources, prompts, and elicitations) to build intelligent, trustworthy systems. This approach is essential for bridging the gap between flexible AI reasoning and robust, production-ready outcomes.