CreditRiskMonitor
NASDAQ listed credit risk dataIn pilotMCP server for regulated credit data
Credit scores, payment scores and peer analysis exposed to AI clients as tool calls.
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Problem
Credit risk data is sold under entitlements. A customer pays for a defined slice of it. The moment an AI client wants that data as a tool call, the entitlement question has to be answered on every single request rather than once at login.
Approach
A Model Context Protocol server that exposes credit scores, payment scores and peer analysis as tools. An AI client calls a tool, the server resolves who is asking and what they are allowed to have, and returns only that.
Architecture
Authentication runs first, then entitlement checks against what that customer actually holds, then the data access itself. Every call is written to an audit log, so there is a record of what left the platform and on whose behalf.
Constraints and scale
Per customer isolation keeps one customer data from reaching another. Rate limits are applied per customer, so a single integration cannot degrade the service for everyone else.
At a glance
- A Model Context Protocol server that turns credit risk data into tool calls an AI client can make directly.
- Authentication, entitlement checks and audit logging on every call.
- Per customer isolation and rate limits, so one customer never sees another customer data and no single integration can crowd out the rest.