Once connected, your AI coding assistant can access the Atono tools exposed by the MCP server. These tools define the specific actions the AI can perform within your workspace.

Setup and context

These tools help the AI connect to your Atono workspace and understand its structure—who’s in it, how teams are organized, and how their workflows are defined.

atono_configuration

Verifies your connection between Atono and the MCP server. When called, it confirms that authentication succeeded and returns basic workspace details such as the workspace name and number of users.

atono_list_users

Retrieves the list of users in your Atono workspace. If you include a team_id, the tool limits results to members of that team. The AI might use this when it needs to confirm who’s available for assignment or to reference teammates by name in follow-up actions.

atono_list_teams

Lists all teams in your Atono workspace and returns each team’s ID, name, and description. The AI can use this information to determine which team owns a story or bug before performing actions such as creating or moving work.

atono_get_team_workflow

Retrieves all workflow steps for a specific team, including their category (To do, In progress, Done, or Won’t do) and whether each step accepts stories, bugs, or new items. This helps the AI understand the team’s workflow before suggesting a move or assignment.

Stories

These tools let the AI retrieve, create, or update stories in your workspace—everything from checking acceptance criteria to moving work between steps.

atono_get_story

Retrieves a story's full details, including its title, handle, acceptance criteria, workflow step, related context like personas or technical notes, and any attachments. The AI might call this tool when summarizing progress, checking acceptance criteria, or confirming which step a story is in. Attachment URLs expire after a short time — re-fetch the story to get fresh ones.

atono_list_story_personas

Retrieves all story personas defined in your Atono workspace. The AI uses this tool before creating or updating stories to confirm valid persona names. If a persona name appears misspelled, the AI will correct it and ask you to confirm before applying changes.

atono_create_story

Creates a new story in your Atono workspace. It can include a title, user story with persona, acceptance criteria, additional content, and optionally a team assignment. Depending on context and the MCP client, the AI may ask for these details or infer them automatically from the conversation.

After creating a story, the AI will immediately call atono_update_ai_context with DESIGN to record the rationale, tradeoffs, and scoping choices made during authoring. To make this more reliable, include it in your request—for example, 'Create a story and capture the design decisions.'

atono_update_story_title

Updates the title of a story. This tool only modifies the title and does not affect the user story content, acceptance criteria, additional content, or any other fields.

atono_update_story_content

Updates the user story—the "As a [persona], I want to [action] so that [benefit]"—of a story. This tool only modifies the user story content area and does not affect acceptance criteria or any other fields. If the changes reflect a meaningful shift in scope or persona, the AI may also update the DESIGN AI context to keep product intent in sync.

atono_update_story_ac

Replaces all acceptance criteria on a story with a new set. Because this tool overwrites the full list, the AI will always retrieve the story first to capture existing criteria IDs before making changes. Omitting a known ID causes a new criterion to be created in its place, which permanently loses any comments, attachments, and usage metrics associated with the original. If the changes reflect a meaningful shift in scope or persona, the AI may also update the DESIGN AI context to keep product intent in sync.

atono_update_story_additional_content

Replaces the additional content of a story—the section below acceptance criteria typically used for background information, technical notes, or other reference material for the team. You can also ask to clear the content from this section.

atono_update_story_team

Assigns or unassigns a story to a team. If a team ID is provided, the story is assigned to that team; if not, it becomes unassigned. Use atono_list_teams to retrieve valid team IDs.

atono_update_story_step

Moves a story to another workflow step within its team. This updates the story’s position in the team’s workflow and affects cycle-time tracking when the step’s category changes.

atono_update_story_assignee

Updates or removes the assignee of a story. If a user ID is provided, the story is assigned to that user; if not, it becomes unassigned. The AI may use this tool to keep ownership up to date when tasks shift between developers.

Bugs

These tools let the AI retrieve, create, update, and document bugs within your Atono workspace, helping it report status or summarize fixes as they’re applied.

atono_list_environments

Retrieves the list of environments defined in your Atono workspace. Results are returned in the workspace-defined order, consistent with the Environments page. The AI uses this tool to find valid environment keys before creating or updating bugs with affected environments.

atono_get_bug

Retrieves a bug's full details, including its title, handle, description, reproduction steps, expected and actual behavior, workflow step, team, affected environments, and any attachments. The AI might use this tool to summarize known issues, report progress, or confirm where a bug currently sits in the workflow. Attachment URLs expire after a short time — re-fetch the bug to get fresh ones.

atono_create_bug

Creates a new bug in your Atono workspace. The title, summary, reproduction steps, expected behavior, and actual behavior are all required by the tool. If you don't provide all of them, the AI will infer reasonable content from context and create the bug—you can refine the details afterward. Affected environments, assignee, and team are optional. If any provided IDs or environment keys aren't recognized, the AI will let you know and suggest how to find valid values. On success, it returns the handle of the created bug.

atono_update_bug

Updates an existing bug's fields. Any combination of title, summary, reproduction steps, expected behavior, actual behavior, team, and affected environments can be updated in a single call. Fields that are omitted remain unchanged. Pass null for team_id to unassign the bug from its current team.

atono_update_bug_step

Moves a bug to another workflow step within its team. This updates its position in the workflow and contributes to cycle-time tracking when the step’s category changes.

atono_update_bug_assignee

Updates or removes the assignee of a bug. If a user ID is provided, the bug is assigned to that user; if not, it becomes unassigned. The AI might use this tool when reassigning work or clarifying ownership during triage.

atono_document_bug_fix

Adds a plain-English summary of a fix to the bottom of a bug’s description. This summary may be added automatically by the AI once a fix is completed. For example: You ask the AI to fix a bug. The AI proposes and applies a fix. The bug is updated to the next workflow step (for example, Test) and a plain-English summary of the fix appears at the bottom of the bug’s description.

Linked items

These tools let the AI create links between related backlog items in your workspace.

atono_link_bugs_or_stories

Links two backlog items in your Atono workspace. Either item may be a story or a bug. Note: the current MCP toolset does not include a way to retrieve existing links—use the Atono UI to view linked items.

Epics

These tools let the AI create and manage epics in your workspace.

atono_create_epic

Creates a new epic. An epic groups related stories under a shared theme or goal, giving teams a way to track larger bodies of work across timeboxes. The AI might use this tool when a request involves multiple related stories that belong together, or when you explicitly ask to organize work under a higher-level initiative.

Attachments

These tools let the AI upload and retrieve files attached to stories and bugs in your workspace. Attaching a file is a two-step process: first upload the file to a staging area, then attach it to a backlog item.

atono_upload_file_url

Retrieves a pre-signed URL for uploading a file to the Atono staging area. The AI uses this URL to PUT the file before attaching it to a story or bug. The staged file is held temporarily until it is attached using atono_create_attachment.

atono_create_attachment

Attaches a staged file to a story or bug. The file must have been uploaded to the staging area first using atono_upload_file_url. An optional position parameter controls where the file appears in the attachment list—start adds it at the beginning, end (the default) adds it at the end. If the file type is unsupported or exceeds the size limit, the tool returns a descriptive error.

atono_get_attachment

Returns information about an attachment, including its file size and content type, along with a download URL for retrieving the file. Use this when you need to access the contents of an attachment already on a story or bug.

Timeboxes

These tools let the AI retrieve timeboxes and their contents, giving it a view of what's planned across your timeline.

atono_list_timeboxes

By default, retrieves all timeboxes in your Atono workspace. You can filter results to active timeboxes only—those in progress or scheduled for the future. The AI might use this tool to find a timebox by name before retrieving its contents, or to give you an overview of what's planned across your timeline.

atono_list_timebox_items

Retrieves all stories and bugs in a given timebox. You can identify the timebox by UUID or by a search string. If the search matches exactly one timebox, that result is used; if multiple matches are found, the AI will ask you to clarify. Each item is returned with its title, handle, workflow step, team, and content. By default both stories and bugs are included, but either can be filtered out.

AI context

These tools let the AI fetch and update AI context on a backlog item. AI context is used to capture AI-assisted design decisions, investigation notes, and implementation changes during work on a story or bug. For usage guidance, see AI context.

Note: DESIGN,INVESTIGATION, and SUMMARY map to the Design decisions, Technical investigation, and Technical changes in the Atono web application.

atono_get_ai_context

Fetches the latest AI context content for a story or bug. Use this at the start of work to pull existing context before investigating or making changes. It can retrieve a DESIGN resource (product and scope decisions recorded during authoring), an INVESTIGATION resource (research notes and findings), or a SUMMARY resource (implementation changes already made).

atono_update_ai_context

Creates or updates AI context content for a story or bug. The AI uses this to keep context current as work progresses. The three resource types serve different purposes:

• DESIGN — Product and scope decisions made during story authoring. Replace or update entries when decisions are revised or superseded. Written by authoring agents; consumed by all agents before and during implementation.
• INVESTIGATION — Current discoveries, research findings, and constraints. Remove anything superseded or proven incorrect.
• SUMMARY — Current record of implementation changes. Preserve all entries unless a change has been explicitly reverted. Synthesis should organize and deduplicate—not compress or drop detail.

Always call atono_get_ai_context before calling atono_update_ai_context. Read the current content, then write a synthesized version—do not append or prepend. Atono retains prior versions automatically.