International Journal of Innovative Research in Computer and Communication Engineering

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TITLE Model Context Protocol (MCP) in Production: Standardizing AI Agent Tool Integration across Enterprise Data Sources, APIS, and Legacy Systems
ABSTRACT The Model Context Protocol (MCP), introduced by Anthropic in November 2024 as an open standard for connecting AI agents with external tools, data sources, and enterprise systems, has achieved unprecedented adoption velocity-surpassing 97 million monthly SDK downloads, powering over 5,800 registered servers, and securing endorsement from Anthropic, OpenAI, Google DeepMind, Microsoft, and AWS within its first year. This rapid proliferation has positioned MCP as the de facto standard for AI agent tool integration, analogous to what the Language Server Protocol (LSP) achieved for IDE-language connectivity. However, production deployments at enterprise scale expose critical challenges in security, performance, governance, and legacy system integration that demand systematic investigation. This paper presents a comprehensive analysis of MCP in production environments, covering architectural foundations, security vulnerability taxonomy (including tool poisoning, prompt injection, rug pull attacks, and OAuth exploitation), performance benchmarks across transport protocols and deployment topologies, and adoption challenges in enterprise contexts with heterogeneous data sources and legacy systems. Through systematic evaluation of published security incidents, specification evolution analysis, and production deployment pattern assessment, we identify twelve critical security patterns, benchmark performance across five deployment configurations, and document seven categories of adoption challenges. Our findings indicate that while MCP provides a technically sound foundation for standardized AI-agent integration, production deployments require defense-in-depth security architectures, explicit human-in-the-loop controls, and purpose-built gateway infrastructure to achieve enterprise-grade reliability. We present actionable mitigation strategies aligned with the OWASP Top 10 for LLM Applications 2025 and propose a maturity model for MCP adoption in regulated enterprise environments.
AUTHOR VENKATA VIJAY SATYANARAYANA MURTHY NEELAM Senior Software Engineer (Cloud, Data, AI/ML, GEN AI), Atlanta, Georgia, USA
VOLUME 171
DOI DOI: 10.15680/IJIRCCE.2025.1306207
PDF pdf/207_Model Context Protocol (MCP) in Production_NEW 1.pdf
KEYWORDS
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