Overview

The searoute_mcp server brings maritime routing capabilities directly into the Model Context Protocol ecosystem, enabling AI assistants such as Claude to query realistic sea‑route data without leaving the conversational interface. By wrapping the open‑source searoute-py library in MCP, it exposes three intuitive tools—, , and —that accept plain latitude/longitude pairs. Internally, the server translates these coordinates into the complex format required by searoute, ensuring that users can focus on their questions rather than data conversion.

For developers building AI‑powered navigation aids, logistics planners, or educational simulations, this server removes the friction of integrating external routing engines. Instead of embedding heavy geospatial libraries or maintaining separate services, an LLM can invoke a single MCP tool to retrieve the shortest oceangoing distance or a full waypoint‑rich route in GeoJSON. The tool provides a quick sanity check by returning the great‑circle distance, which is useful for benchmarking or when land constraints are irrelevant.

Key features include:

• User‑friendly input: All tools accept simple latitude/longitude arguments, making prompts natural for end users.
• GeoJSON output: returns a standard GeoJSON geometry, allowing downstream applications (e.g., mapping libraries or GIS workflows) to consume the route directly.
• Performance: The underlying searoute engine is optimized for large‑scale maritime networks, so the MCP server can handle repeated queries in real time.
• Extensibility: Because it follows the MCP spec, additional tools—such as route optimization under fuel constraints or weather‑aware routing—can be added without changing the client interface.

Typical use cases span from maritime logistics (calculating fuel‑efficient paths between ports) to educational tools that visualize the difference between a great‑circle and an actual sea route. In a shipping company’s AI workflow, a Claude assistant could answer questions like “What is the shortest sea route from Rotterdam to Shanghai?” by calling , then embed the resulting GeoJSON into a dashboard. Similarly, an academic researcher could compare historical shipping lanes with current optimal paths by leveraging both and .

Because the server is built on MCP, it integrates seamlessly with any LLM client that supports the protocol—Claude Desktop, GPT‑based assistants, or custom agents. Developers can deploy it locally for testing or expose it via a streamable HTTP transport for production use, keeping the integration lightweight and secure. The combination of maritime expertise, MCP standardization, and developer‑friendly tooling makes searoute_mcp a standout solution for embedding realistic sea navigation into AI‑driven applications.