Atlantis MCP Server

Overview

The Atlantis MCP Server is a lightweight, Python‑based Model Context Protocol host that bridges local AI assistants with external tools and data sources. By running a single “remote” process, developers can expose custom Python functions or third‑party MCP services as callable tools for Claude, Cursor, Windsurf, or any other MCP‑compatible client. This approach flips the traditional cloud‑centric model: instead of pushing every capability to a remote API, developers retain full control over the execution environment while still benefiting from the shared infrastructure of the Atlantis cloud for discovery and authentication.

What problem does it solve?

Many AI assistants rely on hard‑coded APIs or vendor‑specific SDKs, which limits flexibility and can lock developers into proprietary ecosystems. Atlantis removes this bottleneck by allowing any Python function—whether a simple calculation, a database query, or an external API wrapper—to become an on‑callable tool. The server handles authentication via the Atlantis cloud, automatically synchronizes available tools across machines, and provides a uniform interface that AI agents can consume without needing to understand the underlying implementation details.

Core capabilities and why they matter

• Dynamic function registration: Write a Python routine, register it with the server, and expose it immediately. This eliminates the need for static tool definitions or redeployments.
• Third‑party MCP integration: Store any existing MCP configuration in a JSON file and load it on demand, turning legacy tools into first‑class agents.
• Multi‑remote support: Run several instances on the same host or across a network, each identified by a unique service name. The client automatically discovers and connects to the most appropriate remote.
• Cloud‑backed discovery: The experimental Atlantis cloud acts as a registry, allowing users to share tools and pull updates without manual configuration.
• Secure authentication: Email‑based login combined with an API key (default “foobar” for development) keeps tool usage traceable and isolated to a single user account.

These features give developers granular control over their AI toolchain while keeping the integration process straightforward for end‑users.

Real‑world use cases

• Internal tooling: Expose company‑specific data pipelines or monitoring dashboards as MCP tools, letting agents fetch metrics or trigger alerts on demand.
• Rapid prototyping: Quickly test new algorithms by turning them into tools, iterating without redeploying a full service.
• Hybrid workflows: Combine local computation (e.g., heavy ML inference) with cloud‑hosted services, balancing latency and cost.
• Collaborative projects: Share a library of tools across teams via the Atlantis cloud, ensuring consistent behavior and versioning.

Integration with AI workflows

Clients such as Claude or Cursor can add Atlantis as an MCP server using a simple command (). Once registered, the assistant can invoke any exposed function by name, passing arguments in natural language. The server translates these calls into Python executions or forwards them to other MCP services, returning results in the same format that the assistant expects. Because the server runs locally, latency is minimal, and developers can inspect logs or modify code on the fly without needing to redeploy a cloud function.

Unique advantages

Atlantis stands out by combining the flexibility of local execution with the discoverability and authentication features of a cloud registry. Its design embraces MCP’s peer‑to‑peer nature while still offering centralized control, making it ideal for developers who need both rapid iteration and secure, shareable toolsets. The ability to host multiple remotes on a single machine further enhances scalability, allowing complex workflows to be orchestrated across distinct environments—all through the same MCP interface.