Model Context Protocol

Also known as MCP

Overview

Model Context Protocol research studies how AI agents discover and use tools, data sources, and services through a common client-server interface. Approaches include schema and domain-specific tool design, semantic or hybrid retrieval for large tool catalogs, workflow and graph-based planning, and gateways that handle routing, access control, and errors. Evaluations use benchmarks and live environments to test tool selection, multi-step task completion, efficiency, and recovery, while security work examines server trust and the fidelity of tool descriptions. A central challenge is making MCP systems reliable and safe at scale: agents still struggle with long or changing tool sequences, and outcomes depend on metadata, agent scaffolding, and production infrastructure as well as the protocol itself.

Latest papers

CardsList

32 papers5 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

  1. Tool-Calling LLM Agents Under Realistic MCP Faults: Scale Does Not Purchase Fault Tolerance

    Oct 23, 2026Antonio Ken Iannillo, Joshua Segun Owotogbe, Roberto Natella +3LLM Agent ReliabilityLLM Agent Evaluation

  2. GatewayBench: Unified Capability and Security Evaluation Platform for MCP Gateways

    Oct 16, 2026Yitian Zheng, Zhaorun Chen, Haoyu Li +1Model Context ProtocolAI Agent Security Benchmarks

  3. MCPDisCo: Seesaw-Mitigated Tuning for MCP-Native Agents

    Oct 10, 2026Kounianhua Du, Jianxing Liu, Shuai Shao +3RL for LLM AgentsAgentic RL

  4. Which Metadata Matters for LLM Tool Selection in MCP? Evidence from Controlled Factor-Level Interventions

    Oct 9, 2026Xiaofan Li, Fengchun Qiao, Xing GaoLLM Tool UseModel Context Protocol

  5. MCP Error Messages Written for Developers Hurt the Most Capable Agents Most

    Sep 28, 2026Xiaonan Xu, Wenjing WuLLM Tool UseModel Context Protocol

  6. Cartograph: Federated Tool Discovery with Operator-Attested Retrieval for AI Agents

    Sep 13, 2026Justice Owusu Agyemang, Michael Agyare, Kwame Opuni-Boachie Obour Agyekum +3LLM Tool UseAgentic Retrieval

  7. The Scaffolding Matters More Than the Interface: A Controlled Comparison of MCP and CLI Tool Use Across Seven Agent Scaffoldings, Five Language Models, and One Software Task

    Aug 9, 2026Marc Alier Forment, María José Casañ Guerrero, Francisco José García-Peñalvo +1AI Coding AgentsLLM Agent Evaluation

  8. LLM within MCP Matters: Measuring Inefficient Resource Utilization Driven by LLMs

    Aug 9, 2026Minhan Cho, Soyoung Park, Kihyeon Jeong +3LLM Inference EfficiencyInstruction Following

  9. Scalable LLM Agent Tool Access in the Cloud

    Jul 17, 2026Mingxin Li, Enge Song, Yueshang Zuo +27LLM Tool UseModel Context Protocol

  10. MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers

    Jul 16, 2026Huanxi Liu, Kun Hu, Jiaqi Liao +6Tool-Use EvaluationAI Agent Benchmarks

  11. A Large-Scale Dataset of MCP Implementations on GitHub

    Jul 11, 2026Benny Toeppe, Amine Barrak, Emna KsontiniSoftware EngineeringModel Context Protocol

  12. DynamicMCPBench: A Trace-Grounded, Effect-Scored Benchmark for LLM Agents over Live MCP Servers

    Jul 10, 2026Jerzy Kamiński, Ilya Galyukshev, Artem Kuznetsov +4Computer-Use Agent BenchmarksLLM Agent Evaluation

  13. MCP Server Architecture Patterns for LLM-Integrated Applications

    Jun 29, 2026Carson Rodrigues, Oysturn VasLLM Tool UseModel Context Protocol

  14. A Taxonomy of Runtime Faults in Model Context Protocol Servers

    Jun 3, 2026Joshua Owotogbe, Indika Kumara, Willem-Jan van den Heuvel +3Agent Failure AnalysisModel Context Protocol

  15. Beyond Prompt-Based Planning: MCP-Native Graph Planning-based Biomedical Agent System

    Jun 3, 2026Zhangtianyi Chen, Florensia Widjaja, Wufei Dai +3Model Context ProtocolGraph-Based Planning

  16. MCP-Persona: Benchmarking LLM Agents on Real-World Personal Applications via Environment Simulation

    Jun 1, 2026Wenhao Wang, Peizhi Niu, Gongyi Zou +9LLM Agent EvaluationAI Agent Benchmarks

  17. Resilient Write: A Six-Layer Durable Write Surface for LLM Coding Agents

    Apr 12, 2026Justice Owusu Agyemang, Jerry John Kponyo, Elliot Amponsah +2LLM Agent ReliabilityAI Coding Agents

  18. PHMForge: Evaluating LLM Agents on Industrial Prognostics through MCP-Native, Algorithm-Grounded Tools

    Apr 2, 2026Yusheng Li, Tianjun Feng, Yunfeng Chen +6LLM Agent EvaluationAI Agent Benchmarks

  19. IndustriConnect: MCP Adapters and Mock-First Evaluation for AI-Assisted Industrial Operations

    Mar 25, 2026Melwin Xavier, Melveena Jolly, Vaisakh M A +1Industrial AutomationAI Agent Benchmarks

  20. Bypassing Document Ingestion: An MCP Approach to Financial Q&A

    Mar 19, 2026Sasan Mansouri, Edoardo Pilla, Mark Wahrenburg +1Financial QAModel Context Protocol