galaxy-integration

name: galaxy-integration description: Router for Galaxy MCP, JupyterLite notebooks, and BioBlend automation user_invocable: true

Galaxy Integration Skill

Use Galaxy MCP and BioBlend-based scripts to interact with a Galaxy instance for tool discovery, workflow validation, and workflow testing.

Sub-Skills

Route to the appropriate sub-skill based on task:

When to Use

• Query tools installed on a Galaxy instance
• Inspect tool I/O and parameters
• Create histories, upload data, run tools/workflows
• Debug workflow/tool issues with fast, interactive feedback
• Write JupyterLite notebooks that interact with Galaxy datasets

Prerequisites

Galaxy URL

The base URL of the Galaxy server:

• https://usegalaxy.org/
• https://usegalaxy.eu/
• http://localhost:8080/

Galaxy API Key

In Galaxy UI: User -> Preferences -> Manage API Key

Repository .env (recommended)

cp .env.example .env
nano .env

Set:

GALAXY_URL=https://usegalaxy.org/
GALAXY_API_KEY=your_actual_api_key_here

When to Use MCP vs Scripts

Galaxy MCP when:

• Interactive exploration (find a tool, inspect exact tool IDs)
• Debugging a workflow with fast iteration

BioBlend script (scripts/galaxy_tool_checker.py) when:

• Batch checks across many tools
• Repeatable validation/testing as part of CI-like flow

Core MCP Workflow

1. Confirm MCP connectivity

   • If not connected, see README.md for configuration

2. Tool discovery

   search_tools_by_name(query=...)
   get_tool_details(tool_id=..., io_details=True)
   

3. Workflow testing loop

   create_history(history_name=...)
   invoke_workflow(workflow_id=..., inputs=..., history_id=...)
   get_history_contents(history_id=...)
   # Fix and repeat
   

Output Expectations

When reporting MCP results:

• Provide full Galaxy tool ID (ToolShed-style)
• Include tool version
• Note whether tool is installed vs only available in repository

References

• Upstream Galaxy MCP: https://github.com/galaxyproject/galaxy-mcp
• Repository guide: README.md
• Detailed MCP docs: galaxy-integration.md