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galaxy-integration

Automate Galaxy tool discovery, .ga validation, and workflow testing via BioBlend and MCP.

7|12|Updated Dec 17, 2025
Search Tags:#automation#workflow#integration#MCP#galaxy#tool-check#BioBlend
One-click install
npx skills add https://github.com/galaxyproject/galaxy-skills --skill galaxy-integration
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: galaxy-integration
Source: https://github.com/galaxyproject/galaxy-skills/tree/main/galaxy-integration
Command: npx skills add https://github.com/galaxyproject/galaxy-skills --skill galaxy-integration

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires bioblend, python-dotenv, and includes scripts (resource) components.

What problem does it solve?

This Skill enables programmatic interaction with Galaxy instances by routing tool checks, MCP-based exploration, and BioBlend automation for workflow validation and testing.

Core Features & Use Cases

  • Interactive exploration: Discover tools, inspect I/O, and test workflows against a Galaxy instance.
  • Workflow validation and testing: Validate .ga files for tool availability and run workflows to verify end-to-end results.
  • Agent-friendly orchestration: Guide AI agents through MCP-based tasks or automated pipelines in CI-like environments.

Quick Start

  1. Configure credentials (GALAXY_URL and GALAXY_API_KEY) in a .env file or environment variables.
  2. Use MCP to connect to Galaxy and perform a simple tool search, e.g. search_tools_by_name(query="hyphy").
  3. Validate a workflow: load a .ga file and run validate_workflow, then optionally test_workflow with a history.

Frequently Asked Questions about galaxy-integration

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate Galaxy tool discovery and workflow validation using BioBlend?▼

To automate Galaxy workflow validation, use BioBlend to search tools by name, inspect details, validate .ga files for tool availability, and invoke workflows programmatically. MCP routes these checks to your Galaxy instance for interactive or CI-style testing.

How do I validate a .ga workflow file against a Galaxy instance before running it?▼

Validating a .ga workflow file requires loading it and calling validate_workflow to check tool availability on your Galaxy instance. You can then optionally run test_workflow within a history to verify end-to-end results before deployment.

Can I use MCP to guide AI agents through Galaxy workflow testing and tool checks?▼

Yes, MCP supports agent-friendly orchestration for Galaxy tool checks and workflow testing. AI agents connect to Galaxy instances to perform tool searches, detail lookups, and automated pipeline validation using BioBlend commands.

What credentials do I need to connect to a Galaxy instance for automated workflow testing?▼

Connecting to a Galaxy instance requires your GALAXY_URL and GALAXY_API_KEY. Store these credentials in a .env file or environment variables to authenticate BioBlend and MCP requests for tool discovery and workflow validation.

Does this support Nextflow-to-Galaxy conversions and tool development checks?▼

Yes, automated Galaxy tool discovery and workflow validation apply directly to Nextflow-to-Galaxy conversions and tool development. It performs tool searches and .ga validations across Galaxy deployments to ensure CI-style compatibility.

What are the limitations of using BioBlend and MCP for Galaxy workflow automation?▼

BioBlend and MCP automation for Galaxy workflows requires a configured Galaxy instance and valid API key. Without optional MCP setup, agent-friendly orchestration is limited to direct BioBlend API calls for tool discovery and validation.

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