For Goose AI to be able to access Jira tickets you need an MCP Server. In this workflows we are using MCP server for Atlassian tools.

1. Copy .env.template in .env and open the newly created .env file.
2. Set your Gemini key in GOOGLE_API_KEY (take it from Google Cloud -> API & Services -> Credentials -> API Keys -> show key)
3. Set your Jira Personal Token in JIRA_PERSONAL_TOKEN (create PATs in your Jira/Confluence profile settings - usually under "Personal Access Tokens")
4. Change (if needed) the JIRA_URL now pointing at https://issues.redhat.com/
5. Set your Gitlab Personal Token GITLAB_TOKEN with read permissions (read_user, read_repository, read_api).

If you need to change the llm provider and model, they are stored in the Goose config file: goose-container/goose-config.yaml (GOOSE_PROVIDER, GOOSE_MODEL)

If you want to use Goose AI with remote recipes, set the repo from where to take the recipes in goose-container/goose-config.yaml -> GOOSE_RECIPE_GITHUB_REPO with username/repo. Warning: while developing it is difficult to use a remote recipe (since it has not yet been deployed in main or merged in target repo, I have found no way to dinamically set a branch or fork for playing with the recipe).

make build

Run the Jira MCP server from Atlassian and Goose separately, otherwise not all the input from your terminal is always redirected to the Goose container.

1. make run-mcp-atlassian
2. make run-goose
3. Type List all In Progress issues at https://issues.redhat.com/projects/LD and wait for the output.
4. make clean

You can further manually run test and run the Goose recipes which are mounted into the container at /home/goose/recipes.

The recipes are defined in goose-recipes/. If you want to run goose-recipes/<recipe>.yaml, run the following:

1. make <recipe>
2. make clean