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 . The bioinformatics landscape is characterized by an extensive ecosystem of specialized tools designed for diverse analytical tasks that serve critical functions in genomics [1], proteomics [2], [3], and molecular biology [4], and so on.

Access to content on Oxford Academic is often provided through institutional subscriptions and purchases. Typically, access is provided across an institutional network to a range of IP addresses. This authentication occurs automatically, and it is not possible to sign out of an IP authenticated account. Choose this option to get remote access when outside your institution. Shibboleth / Open Athens technology is used to provide single sign-on between your institution's website and Oxford Academic.

Access to restricted content on Oxford Academic is often provided through institutional subscriptions and purchases. Typically, access is provided across an institutional network to a range of IP addresses. This authentication occurs automatically, and it is not possible to sign out of an IP authenticated account. Choose this option to get remote access when outside your institution. Shibboleth / Open Athens technology is used to provide single sign-on between your institution's website and Oxford Academic.

Leading experts in cybersecurity and ethics from Oxford Internet Institute, University of Oxford, Dr Mariarosaria Taddeo and Professor Luciano Floridi, and Professor Tom McCutcheon from Defence Science and Technology Laboratories believe the current approach to defining standards and certification procedures for Artificial Intelligence (AI) systems in cybersecurity is risky and should be replaced with an alternative method. Their new paper "Trusting Artificial Intelligence in Cybersecurity: a Double-Edged Sword", published in the journal Nature Machine Intelligence argues that defining standards based on placing implicit trust in AI systems to perform as expected, without any degree of any monitoring or control, could leave us at risk of new forms of AI attacks, disrupting systems and changing their behaviour. Current'trust' based standards and certification procedures in AI typically see tasks being carried out with either no or minimal control on the way the AI-driven tasks are performed. In their paper, the cybersecurity experts present the case for developing'reliable' rather than trustworthy AI in cybersecurity. The experts argue that reliable AI has greater potential to ensure the successful deployment of AI systems for cybersecurity tasks, making them less vulnerable to cyber-attacks.

Health and Retirement Study participants, aged 65-90, with DNA and 2 cognitive evaluations, were included (n 7,142). Predictors included age, body mass index, gender, education, APOE ε4, CVD, hypertension, diabetes, stroke, neighborhood socio-economic status(NSES), and AD risk genes. Latent class trajectory analyses of cognitive scores determined the form and number of classes. Random Forests (RF) classification investigated predictors of cognitive trajectories. Performance metrics (accuracy, sensitivity and specificity) were reported.

Access to restricted content on Oxford Academic is often provided through institutional subscriptions and purchases. Typically, access is provided across an institutional network to a range of IP addresses. This authentication occurs automatically, and it is not possible to sign out of an IP authenticated account. Choose this option to get remote access when outside your institution. Shibboleth / Open Athens technology is used to provide single sign-on between your institution's website and Oxford Academic.