nf-core Tools MCP

Overview

The Mcp Nf Core server brings the power of nf‑core pipelines and modules directly into AI assistant workflows. By exposing a curated set of commands through the Model Context Protocol, it lets developers ask an assistant to scaffold pipelines, discover available modules, and embed reusable components—all without leaving the conversational interface. This eliminates manual cloning or template setup, streamlining the early stages of bioinformatics project development.

At its core, the server implements four primary operations:

• Create a pipeline from the official nf‑core template, ensuring consistent project structure and best practices.
• List available modules so users can quickly see what reusable bioinformatics workflows exist in the community.
• Create a module using the standard nf‑core modules template, allowing contributors to add new analyses with minimal friction.
• Install a module into an existing pipeline or modules repository, automatically handling dependency resolution and configuration updates.

These tools are available through two back‑end implementations—one that invokes the nf‑core CLI and another that calls the underlying Python library directly. Both expose identical MCP endpoints, giving developers flexibility to choose the most suitable runtime for their environment.

For practitioners building AI‑augmented pipelines, this server offers a seamless bridge between conversational commands and reproducible workflow artifacts. A researcher can ask the assistant, “Create a new nf‑core pipeline for RNA‑seq analysis,” and receive a ready‑to‑run project skeleton, complete with Dockerfile, documentation, and test cases. A bioinformatician can request a list of modules that perform variant calling, select one, and have it installed into their pipeline—all while keeping version control and metadata intact.

The server’s design prioritizes reusability, consistency, and community alignment. By leveraging nf‑core’s rigorous template system, every generated pipeline adheres to the same standards used by thousands of open‑source projects. This ensures that code produced through AI commands is immediately compatible with existing nf‑core tooling, CI pipelines, and deployment environments. Additionally, the dual implementation strategy means that projects can run on lightweight CLI setups or within Python‑centric workflows without sacrificing feature parity.

In practice, the Mcp Nf Core server is ideal for rapid prototyping, teaching bioinformatics workflows, and integrating standardized pipelines into larger AI‑driven data processing stacks. Whether you’re a seasoned developer or a new user, the server turns natural language requests into fully functional nf‑core projects, saving time and reducing errors in complex pipeline construction.