# Survey of LLM Agent Communication with MCP: A Software Design Pattern Centric Review

arXiv: https://arxiv.org/abs/2506.05364 · HTML: https://mlsanity.com/paper/2506.05364/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 3 of its 10 references that are in the map.
- First posted on arXiv 26 May 2025.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 3 of 3)

- [Multi-Agent Collaboration Mechanisms: A Survey of LLMs](https://mlsanity.com/paper/2501.06322/) (2025)
- [AgentNet: Decentralized Evolutionary Coordination for LLM-based Multi-Agent Systems](https://mlsanity.com/paper/2504.00587/) (2025)
- [Beyond Self-Talk: A Communication-Centric Survey of LLM-Based Multi-Agent Systems](https://mlsanity.com/paper/2502.14321/) (2025)

## Built on by (most cited first, 14 of 14)

- [A Survey of Context Engineering for Large Language Models](https://mlsanity.com/paper/2507.13334/) (2025)
- [A Survey of LLM-Driven AI Agent Communication: Protocols, Security Risks, and Defense Countermeasures](https://mlsanity.com/paper/2506.19676/) (2025)
- [Model Context Protocol (MCP) Tool Descriptions Are Smelly! Towards Improving AI Agent Efficiency with Augmented MCP Tool Descriptions](https://mlsanity.com/paper/2602.14878/) (2026)
- [JIT-Agent: Scaling Harness Intelligence via Just-in-Time Harness Evolution](https://mlsanity.com/paper/2608.25593/) (2026)
- [AgentMaster: A Multi-Agent Conversational Framework Using A2A and MCP Protocols for Multimodal Information Retrieval and Analysis](https://mlsanity.com/paper/2507.21105/) (2025)
- [Semantic Attacks on Tool-Augmented LLMs: Securing the Model Context Protocol Against Descriptor-Level Manipulation](https://mlsanity.com/paper/2512.06556/) (2025)
- [Don't believe everything you read: Understanding and Measuring MCP Behavior under Misleading Tool Descriptions](https://mlsanity.com/paper/2602.03580/) (2026)
- [Red Skills or Blue Skills? A Dive Into Skills Published on ClawHub](https://mlsanity.com/paper/2604.13064/) (2026)
- [Designing LLM-based Multi-Agent Systems for Software Engineering Tasks: Quality Attributes, Design Patterns and Rationale](https://mlsanity.com/paper/2511.08475/) (2025)
- [Give Them an Inch and They Will Take a Mile:Understanding and Measuring Caller Identity Confusion in MCP-Based AI Systems](https://mlsanity.com/paper/2603.07473/) (2026)
- [Co-TAP: Three-Layer Agent Interaction Protocol Technical Report](https://mlsanity.com/paper/2510.08263/) (2025)
- [STEM Agent: A Self-Adapting, Tool-Enabled, Extensible Architecture for Multi-Protocol AI Agent Systems](https://mlsanity.com/paper/2603.22359/) (2026)
- [Uncertainty Quantification for LLM Agents: A Taxonomy, an Evaluation Protocol, and an Empirical Study](https://mlsanity.com/paper/2609.07395/) (2026)
- [A Hybrid Agentic AI Framework for Intelligent Supply Chain Analytics](https://mlsanity.com/paper/2609.13561/) (2026)

## Abstract (arXiv)

This survey investigates how classical software design patterns can enhance the reliability and scalability of communication in Large Language Model (LLM)-driven agentic AI systems, focusing particularly on the Model Context Protocol (MCP). It examines the foundational architectures of LLM-based agents and their evolution from isolated operation to sophisticated, multi-agent collaboration, addressing key communication hurdles that arise in this transition. The study revisits well-established patterns, including Mediator, Observer, Publish-Subscribe, and Broker, and analyzes their relevance in structuring agent interactions within MCP-compliant frameworks. To clarify these dynamics, the article provides conceptual schematics and formal models that map out communication pathways and optimize data flow. It further explores architectural variations suited to different degrees of agent autonomy and system complexity. Real-world applications in domains such as real-time financial processing and investment banking are discussed, illustrating how these patterns and MCP can meet specific operational demands. The article concludes by outlining open challenges, potential security risks, and promising directions for advancing robust, interoperable, and scalable multi-agent LLM ecosystems.