• New MCP server connects external AI tools directly to structured laboratory data, with no exports or custom integrations required
• Suite of AI functions address key data bottlenecks in biological research labs, from protocol writing to equipment set-up
• The launch also marks Lab Thread’s incorporation in the United States, extending support for the company’s growing base of US life sciences customers

Lab Thread Ltd, a UK-based life science software company, today announced the launch of a new Model Context Protocol (MCP) server that lets scientists connect their own AI assistants directly to their laboratory data. The launch coincides with Lab Thread’s formal incorporation in the United States, and opening of a Boston office, enabling US-based customers to engage with Lab Thread under a local entity, and extending support for the Company’s growing base of customers in the region.

Lab Thread’s MCP server uses the same open standard that AI assistants such as ChatGPT, Claude and others use to connect to external software. This means scientists can point the tools they already use at their own lab data and query it directly, without exporting records or commissioning a custom integration. Because Lab Thread stores data as structured, linked records rather than free text, answers come back with full experimental context attached.

Security and intellectual property protection are embedded in Lab Thread’s server architecture. The MCP server operates within the Company’s secure cloud environment, enforcing tenant isolation, granular role-based permissions, and immutable audit logs aligned with 21 CFR Part 11 and ALCOA++ compliance frameworks. Data remains within Lab Thread until an authorised query is made, at which point only the minimum necessary context is shared with the external AI assistant chosen by the user.

Alongside the MCP server, Lab Thread has released a suite of new AI functions designed to simplify some of the more time-consuming and labour-intensive aspects of onboarding a new software platform and maintaining accurate scientific records. Using these tools, scientists can now generate experimental protocols and ELN templates, configure equipment and storage locations, and transcribe handwritten notes directly into formatted lab notebook entries within the Lab Thread platform.

Dr Ryan Cawood, Co-founder and CEO of Lab Thread, said: “No matter how intuitive a platform is, getting up and running is still where most digitisation projects stall. That’s exactly the feedback we hear from our own customers: once they’re in, they’re often teaching themselves within days, with barely any training needed. With the MCP server, and new AI functionality, we’re making that even easier: whatever AI assistant a scientist already uses, Lab Thread makes sure it can see their data, securely and in context, from day one.”

Since its worldwide launch in July 2026, Lab Thread has been adopted by research teams at institutions including Mayo Clinic and UMass Chan Medical School.

Lab Thread connects bench scientists, laboratory leadership and C-suite decision-makers in a single platform, with modules spanning project management, molecular and protein engineering, electronic lab notebooks (ELN), laboratory information management (LIMS), sample tracking, and inventory and equipment management. The platform’s underlying data architecture structures all records in an AI-ready format, enabling laboratories to integrate AI into existing workflows without costly data cleaning or migration.