Surf MCP Server

The Surf MCP Server fills a niche that many AI‑powered surf enthusiasts and travel planners find frustrating: the lack of an easy, programmatic way to pull accurate tide data into conversational agents. By exposing a lightweight MCP endpoint that queries the Storm Glass API, this server turns tide forecasts into a first‑class tool for Claude and other Model Context Protocol clients. Developers can now ask an AI assistant, “When is the next high tide near Bondi?” and receive a structured, timestamped response without leaving the chat.

At its core, the server offers a single, well‑defined tool that accepts geographic coordinates and an optional date. It returns high‑and‑low tide times, their heights in meters, and contextual station information such as name and distance. Because the tool internally normalises all timestamps to UTC, developers can avoid common pitfalls related to time‑zone conversions—a frequent source of errors in surf‑related workflows. The server also respects Storm Glass’s rate limits, so it can be safely used in production environments or for batch analysis of multiple locations.

Key capabilities include:

• Location‑specific queries: Latitude and longitude inputs let the tool target any point on Earth, making it useful for both local beachgoers and international travelers.
• Date filtering: By passing a string, users can retrieve forecasts for future dates or historical data, supporting planning and retrospective analysis.
• Rich metadata: The response includes not just tide times but also station details and distances, enabling downstream logic such as recommending the nearest suitable break.
• Automatic UTC handling: All times are returned in Coordinated Universal Time, simplifying integration with other time‑sensitive systems.

Real‑world scenarios that benefit from Surf MCP are plentiful. A surf trip planner could integrate the tool into a chatbot to suggest optimal departure times, while an environmental research pipeline might batch‑query tides across multiple monitoring stations for data analysis. Even a simple weather‑forecasting assistant can enrich its responses by appending tide information, providing users with a holistic view of conditions that affect wave quality.

Integrating Surf MCP into an AI workflow is straightforward: the server registers itself as a FastMCP tool, and any MCP‑compatible client can invoke it by passing the required parameters. The tool’s output is a plain string that can be parsed or directly displayed, allowing developers to embed tide data into natural language responses, dashboards, or automated alerts. Because the server is built on standard Python tooling and can be launched via , it fits neatly into existing CI/CD pipelines or local development environments.

What sets Surf MCP apart is its focus on surf‑specific data combined with an easy, time‑zone‑aware interface. Rather than forcing developers to write custom API wrappers or handle complex JSON schemas, the server delivers concise, actionable tide information ready for consumption by AI assistants. This reduces friction for developers looking to add surf‑related intelligence to their applications and empowers users with precise, trustworthy tide forecasts at the touch of a button.