Frankfurter MCP

Frankfurter MCP bridges the gap between AI assistants and real‑time foreign exchange data by exposing the Frankfurter API through the Model Context Protocol. The API provides up‑to‑date currency exchange rates, historical tables, and time‑series data from reputable sources such as the European Central Bank. By turning this functionality into a first‑class MCP server, developers can let language models retrieve, transform, and reason about financial information without leaving the agent’s native environment.

The server exposes a concise set of tools that map directly to Frankfurter endpoints: fetching the latest rates, querying historical snapshots for a given date or range, and obtaining time‑series data in a format that is immediately usable by downstream processing. Each tool accepts simple JSON arguments and returns structured responses, enabling agents to compose complex financial workflows—such as currency conversion calculations, trend analysis, or risk assessment—within a single conversational turn. Because the MCP interface handles authentication, request throttling, and error handling internally, developers can focus on business logic rather than plumbing.

Key capabilities include:

• Real‑time rate retrieval – obtain current exchange rates for any currency pair with minimal latency.
• Historical data access – fetch past rates or full tables for any date, supporting back‑testing and retrospective analysis.
• Time‑series generation – request continuous data over a date range, ideal for charting or trend detection.
• Robust configuration – environment variables let you tune timeouts, SSL verification, and logging to match production or development needs.
• FastMCP integration – built on FastMCP, the server benefits from efficient transport layers (HTTP or stdio) and automatic request routing.

In practice, Frankfurter MCP is invaluable for fintech startups building AI‑driven chatbots that need instant currency conversion, budgeting assistants that track exchange fluctuations, or compliance tools that audit foreign‑exchange exposure. By integrating the server into an AI workflow, agents can query rates on demand, embed them in financial narratives, or trigger downstream actions (e.g., placing a trade) based on the retrieved data—all while maintaining a seamless conversational experience.

The server’s lightweight design and clear API surface make it easy to deploy in containerized environments or as a local development stub. Its compatibility with existing MCP tooling (prompts, sampling strategies, and resource management) ensures that developers can incorporate Frankfurter MCP into larger multi‑tool agent ecosystems without friction.