PythonAnywhere Model Context Protocol Server

The PythonAnywhere MCP server bridges the gap between language‑model assistants and the PythonAnywhere cloud platform. By exposing a uniform MCP interface, it lets AI tools perform common hosting tasks—file manipulation, web‑app deployment, and scheduled job management—directly from a conversation. This eliminates the need for manual SSH or web‑interface interactions, enabling rapid iteration and automation while keeping all actions auditable through the platform’s own logging.

Developers benefit from having a single, well‑defined API that mirrors the capabilities they already use in PythonAnywhere’s web UI. For example, a Claude assistant can read or upload files to a project, restart an ASGI web‑app with a simple command, or set up a cron job to run nightly data imports. The server’s design follows the MCP specification, so any compliant client (Claude Desktop, Cursor, or custom tooling) can talk to it without bespoke adapters. This lowers the barrier to integrating cloud hosting into AI‑driven workflows and lets teams keep a tight feedback loop between code changes and live deployments.

Key features are grouped around the most common PythonAnywhere operations:

• File management: Read, upload, delete, and list directory trees. This is useful for debugging logs or quickly updating configuration files.
• ASGI web‑app control: Create, delete, reload, and list apps—mirroring the commands available in the PythonAnywhere help pages.
• WSGI web‑app support: Currently limited to reloading, but still valuable for legacy projects.
• Scheduled task administration: Full CRUD over scheduled jobs. This allows an LLM to set up periodic tasks, though the README cautions about timing pitfalls and suggests using a time‑aware MCP server for precise control.

Typical use cases include:

• Rapid prototyping – a developer can ask an assistant to spin up a new web app, push code, and reload it in one interaction.
• Continuous deployment – an LLM can monitor a GitHub repository, pull changes to PythonAnywhere, and restart the app automatically.
• Operational automation – scheduled tasks can be created or adjusted on demand, freeing operators from manual cron configuration.

Integration is straightforward for any MCP‑aware client. Once the server is running, it presents itself as a standard MCP endpoint; language models can then invoke actions using the same prompt patterns they use for other tools. The server’s reliance on environment variables (API token and username) keeps credentials out of code, while the audit trail provided by PythonAnywhere’s logs ensures transparency.

In summary, the PythonAnywhere MCP server turns a cloud hosting platform into an AI‑friendly resource. It streamlines development workflows, reduces friction between code and deployment, and opens the door to fully automated, model‑driven operations—all while maintaining the safety nets that come with controlled API access.