AML Watcher MCP Server

The AML Watcher MCP server bridges the gap between AI assistants and real‑time anti‑money‑laundering (AML) intelligence. By exposing a Dockerised AML screening engine as an MCP endpoint, the server lets developers query for suspicious entities, watchlists, and compliance reports directly from Claude or other AI clients. This eliminates the need to build custom API wrappers, enabling rapid integration of AML data into conversational workflows.

At its core, the server accepts a set of environment variables that tailor the screening process. The most critical is , which authenticates requests against AML Watcher’s service. Additional filters—such as , , and boolean switches for and —allow fine‑grained control over the returned results. The and parameters shape pagination and similarity thresholds, giving developers the ability to balance latency against coverage. These variables are passed into a Docker container () that performs the heavy lifting of querying the AML database and formatting the response for the AI assistant.

Developers benefit from a plug‑and‑play architecture: once the MCP configuration is added to , Claude can invoke the server with simple tool calls. The AI can ask questions like “Show me recent PEP Level 1 alerts in Canada” and receive structured JSON results without any additional code. This streamlines compliance workflows, allowing analysts to surface relevant alerts during conversations or automate remediation steps.

Real‑world scenarios include onboarding new employees, where an AI assistant can quickly verify potential sanctions risks; monitoring transaction streams in fintech apps, providing instant risk flags; or supporting legal teams with up‑to‑date watchlist checks during case preparation. Because the server runs inside Docker, it scales independently and can be updated without touching the AI client, ensuring that the latest AML data is always accessible.

Unique to this MCP server are its tightly coupled environment‑driven configuration and the Docker isolation that guarantees consistent performance across platforms. The ability to expose a full suite of AML filters through simple key‑value pairs makes it especially valuable for developers who need to integrate complex compliance logic into conversational AI without wrestling with HTTP clients, authentication flows, or data parsing.