Architecture & Execution

Architecture & Execution

📌 TL;DR: Peek under the hood of MCP to learn how user prompts are processed, tools are selected, and execution is handled, with both low-code and power-user options.

What happens behind the scenes when you issue an MCP command?

When you send a natural language request like: “Scan socradar.io for open ports and summarize the results” …a multi-stage execution process begins under the hood:

• Task Parsing:

{ “task”: “port_scan”, “target”: “socradar.io”, “scan_type”: “tcp_syn”, “output_format”: “summary” }

• Server Selection: The agent chooses the best-suited MCP Server from the available registry.

• Command Execution: The MCP Server translates the task into native commands (e.g., Nmap syntax), executes it, and formats the output into MCP-compatible JSON.

• Response Handling: The result is passed back to the agent, which may summarize, enrich, or escalate it depending on the flow.

In short, MCP acts as a “translator and dispatcher,” allowing the agent to execute powerful actions across systems without custom coding or manual input.

Role of orchestrators like CrewAI, LangGraph, AutoGen

Orchestrators take MCP to the next level by chaining multiple tasks, applying logic, and maintaining memory between steps. Here’s how each contributes:

Orchestrator	Purpose
CrewAI	Assigns specific tasks to named agents (e.g., Recon Agent, Intel Analyst). Ideal for red teaming and SOCs.
LangGraph	Creates stateful, visual task flows with conditional routing. Great for multi-stage playbooks.
AutoGen	Focuses on AI-to-AI collaboration, useful for dynamic role assignment and prompt negotiation.

MCP Servers serve as tools within these orchestrators. Example: A Security Analyst Agent in CrewAI may use the nmap_scan MCP Server first, then pass results to a Report Agent that summarizes findings using Claude.

How does an MCP server select which tool or API to use?

Tool selection is either implicit or explicit.

• Implicit (default) The agent decides which server to invoke based on:
• • Tool availability
  • Past performance
  • Cost-efficiency
  • Security level
• Explicit (power users) The user can hard-code tool preference:

{ “task”: “port_scan”, “target”: “socradar.io”, “server_hint”: “nmap-cloud”, “scan_type”: “udp” } Some servers also expose capability metadata, which helps agents auto-select based on task compatibility. Over time, this tool-routing layer will evolve to support scoring, fallback options, and fine-tuned performance heuristics, similar to a load balancer but for agents and tools.