BioinfoMCP: A Unified Platform Enabling MCP Interfaces in Agentic Bioinformatics

2025-10-02

AI Summary

Biological tools and AI-agent frameworks suffer from interface incompatibility, heterogeneous I/O formats, and inconsistent parameter conventions, impeding interoperability. To address this, we propose the first MCP (Model Context Protocol)-oriented framework for automated generation and systematic validation of bioinformatics services. Leveraging large language models to parse tool documentation, our framework integrates standardized semantic mapping with MCP specifications to construct an end-to-end translation and benchmarking pipeline. It enables the first fully automated, batch conversion of documentation into deployable, compliant MCP services, accompanied by a rigorous validation methodology. We successfully transformed 38 widely used bioinformatics tools into MCP-compliant services; 94.7% demonstrate stable performance across three major AI-agent platforms, supporting complex analytical workflows. This significantly enhances tool interoperability and natural-language-driven execution, establishing foundational infrastructure for intelligent bioinformatics.

Technology Category

• Multiagent Systems: Modeling other Agents
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search

Abstract

Bioinformatics tools are essential for complex computational biology tasks, yet their integration with emerging AI-agent frameworks is hindered by incompatible interfaces, heterogeneous input-output formats, and inconsistent parameter conventions. The Model Context Protocol (MCP) provides a standardized framework for tool-AI communication, but manually converting hundreds of existing and rapidly growing specialized bioinformatics tools into MCP-compliant servers is labor-intensive and unsustainable. Here, we present BioinfoMCP, a unified platform comprising two components: BioinfoMCP Converter, which automatically generates robust MCP servers from tool documentation using large language models, and BioinfoMCP Benchmark, which systematically validates the reliability and versatility of converted tools across diverse computational tasks. We present a platform of 38 MCP-converted bioinformatics tools, extensively validated to show that 94.7% successfully executed complex workflows across three widely used AI-agent platforms. By removing technical barriers to AI automation, BioinfoMCP enables natural-language interaction with sophisticated bioinformatics analyses without requiring extensive programming expertise, offering a scalable path to intelligent, interoperable computational biology.

Problem

• Integrating bioinformatics tools with AI agents faces interface incompatibility issues
• Manual conversion of tools to MCP standard is labor-intensive and unsustainable
• Enabling natural-language interaction with bioinformatics tools requires automated solutions

Innovation

• Automatically generates MCP servers from documentation
• Systematically validates tool reliability across diverse tasks
• Enables natural-language interaction with bioinformatics analyses