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Cross-Server Interoperability in Multi-MCP Automated AI Agent Networks

by Prudhvi Ratna Badri Satya, Ajay Guyyala, Krishna Teja Areti, Vijay Putta
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 84
Year of Publication: 2026
Authors: Prudhvi Ratna Badri Satya, Ajay Guyyala, Krishna Teja Areti, Vijay Putta
10.5120/ijca2026926474

Prudhvi Ratna Badri Satya, Ajay Guyyala, Krishna Teja Areti, Vijay Putta . Cross-Server Interoperability in Multi-MCP Automated AI Agent Networks. International Journal of Computer Applications. 187, 84 ( Feb 2026), 1-14. DOI=10.5120/ijca2026926474

@article{ 10.5120/ijca2026926474,
author = { Prudhvi Ratna Badri Satya, Ajay Guyyala, Krishna Teja Areti, Vijay Putta },
title = { Cross-Server Interoperability in Multi-MCP Automated AI Agent Networks },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2026 },
volume = { 187 },
number = { 84 },
month = { Feb },
year = { 2026 },
issn = { 0975-8887 },
pages = { 1-14 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number84/cross-server-interoperability-in-multi-mcp-automated-ai-agent-networks/ },
doi = { 10.5120/ijca2026926474 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2026-02-21T01:28:19.414594+05:30
%A Prudhvi Ratna Badri Satya
%A Ajay Guyyala
%A Krishna Teja Areti
%A Vijay Putta
%T Cross-Server Interoperability in Multi-MCP Automated AI Agent Networks
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 84
%P 1-14
%D 2026
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper introduced a combined framework for cross-server interoperability in multi-MCP automated AI agent networks. The design combined communication abstraction, orchestration optimization, and security validation. The framework was tested on BoT-IoT, ToN-IoT, and PettingZoo datasets, which represented adversarial traffic detection, telemetry-heavy IoT environments, and dynamic multi-agent orchestration. Results showed improvements in coverage, efficiency, and robustness, with accuracy, precision, recall, and F1-score above 0.95 across multiple trials. Ablation analysis confirmed the role of each component, scalability tests showed stable performance as servers increased, and stress evaluations demonstrated graceful degradation under heavier attack loads. Error analysis and statistical validation supported the reliability of the outcomes, while resource usage comparisons indicated reduced runtime and memory consumption against baselines. Cross-domain generalization confirmed adaptability across unseen datasets. These findings demonstrated that interoperability in heterogeneous MCP networks can be achieved without sacrificing efficiency, scalability, or reliability, providing a foundation for secure and practical multi-domain agent collaboration.

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Index Terms

Computer Science
Information Sciences

Keywords

Model Context Protocol (MCP) Multi-Agent Systems Cross- Server Interoperability Automated AI Agents IoT Security Orchestration Distributed Systems

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