Project description
Today, LCH generates and distributes thousands of static reports (PDF, CSV, XML) via SFTP and portal downloads. This model is expensive, increasingly unfit for purpose—delivering static data outputs rather than actionable, real-time intelligence. • Stale, batch-driven data - reports generated ITD or EOD overnight (T+1), limiting timely decision-making • Operationally heavy & fragile delivery - high cost to generate, store, and manage files; distribution is prone to failures, delays, format issues and subsequent ingestion by members can fail as well. • High client and support friction - fixed report formats with no flexibility; frequent support queries and slow turnaround for ad-hoc requests • No programmatic or intelligent access - data is locked in files, with no ability to query, explore, or integrate dynamically into member systems This results in a fragmented, inefficient experience, where effort is spent in generating files and accessing data rather than deriving value. Remediation is proposed through the introduction of an MCP facade over LCH's APIs, allowing control to be retained, AI-driven intelligence to be enabled, and how clients access and use data to be fundamentally transformed. This is not a reporting upgrade—it is a platform shift.
Responsibilities
SKILLS
Must have
Nice to have
experience of Model Context Protocol (MCP)