How to build an MCP-style system for intelligent AI agents

MarkTechPost developers have prepared a comprehensive guide for building an MCP-style AI agent system. This is not just an agent—it's an ecosystem with tool discovery, intelligent routing, planning, and task execution. The result works as a unified mechanism where each part handles its own stage.

Modular Tool Server

At the heart of the system lies a modular server that provides the agent with everything necessary. The server works as a hub through which the agent gains access to tools. Each tool is described through structured definitions—this way the agent knows what it can do, which parameters to pass, and what result to expect.

The system supports several types of tools. Web search allows searching for information on the internet. Local retrieval works with documents and knowledge bases on the server. Dataset loading helps work with data for analysis. Python execution enables running code directly on the server. All tools use the same interface—this allows the agent to switch between them without retraining.

Four Stages of Operation

The process works according to a strict scheme of four stages:

• Tool discovery—the agent scans the server and discovers available capabilities
• Routing—the system decides which tool is suitable for the task
• Planning—the agent creates a step-by-step execution plan
• Execution—executing the plan with result verification

The first stage is tool discovery. The agent scans the server and discovers what capabilities are available to it. In the second routing stage, the system analyzes the task and selects the appropriate tool or combination. The third stage is planning: the agent creates a step-by-step plan because most tasks require multiple steps. The fourth stage is execution, when the agent carries out the plan and verifies the results of each step.

Dynamic Context Injection

The key idea is to not pass all context at once. Instead, at each stage exactly what is needed right now is added to the agent's memory. At the tool discovery stage the agent sees only the list of available capabilities. At the planning stage it sees the goal and tools, but not the full context of the entire system. At the execution stage it sees the results of previous steps.

This approach reduces the load on the model and makes decisions more focused. The agent is not distracted by information it doesn't need right now. Results become better and the cost of operation is lower.

What This Means

MCP-style architecture allows building agents that work more efficiently and reliably. This is not just a new tool—it's a new class of systems where each component is responsible for its own part of the work. Adding a new tool is as simple as defining its structure, and the system will automatically understand when to use it.