Akurateco has made its payment API documentation readable by AI coding assistants through the Model Context Protocol (MCP).
The move allows developers integrating the company’s payment API to query it from within the tools they already use for development work.
The technology behind
The capability is built on the MCP, an open standard for connecting AI assistants to external data sources. Through this connection, developers using Claude Code, Codex, Cursor, and other MCP-compatible tools can ask natural-language questions about endpoints, parameters, and integration processes. Because responses come from the live documentation rather than from data a model was trained on, the update is designed to prevent a common issue in AI-assisted development: an assistant citing an endpoint that has since been changed or that was never part of the actual specification.
To simplify adoption, the documentation portal offers a one-click option that generates the setup configuration for a developer's preferred AI tool. Akurateco has also published a separate explainer on how MCP functions, given that many integration teams have limited exposure to the standard so far.
Relevance for integration teams
The update targets technical staff working for payment service providers (PSPs), acquiring banks, and enterprise merchants that use Akurateco's white-label infrastructure. As AI-assisted development becomes more common in payments integration work, such as linking acquirers, alternative payment methods, and internal systems, machine-readable documentation could reduce the time developers spend searching for correct technical details before completing a first test transaction.
Andrew Riabchuk, Founder and CTO at Akurateco, noted that much of the time spent on integration goes not into writing code, but into finding the right information, and that giving an AI assistant direct access to accurate documentation removes some of that uncertainty.
The move reflects a broader trend for the financial software world to make technical documentation machine-readable for AI agents, rather than solely for human developers, as AI-assisted coding tools become more embedded in integration and development workflows across the payments sector. For platforms such as Akurateco's, which depend on third-party integration teams connecting multiple acquirers and payment methods, reducing documentation lookup friction has a direct bearing on integration timelines.
