Introducing Atono's MCP Server: your AI can streamline your workflow

Individual speed keeps improving, but team velocity hasn't caught up.

Developers fly through code with AI assistants like Claude, Cursor, Windsurf, and Copilot. But coordination still happens the old way — manual updates, browser tabs, copy-paste workflows.

The result? Your developers are fixing 10 bugs a day but spending 50 minutes just managing the coordination around those fixes.

That's not productive friction. That's just friction.

Until now, AI assistants and workflow tools spoke different languages. AI could write a fix, but it couldn't move the story, update the assignee, or document the change — because your workflow data lived somewhere else.

The Atono MCP Server bridges that gap.

The MCP Server runs locally using Docker and connects your AI tools to your Atono workspace through the Model Context Protocol (MCP) — an emerging open standard for secure AI-to-system integration.

Think of it as an interpreter between your AI assistant and your workflow.

Once connected, your AI can:

• Create stories from plain English descriptions

• Move work through workflow steps on your team's board

• Assign or reassign teammates without leaving your editor

• Document fixes automatically in natural language

• Retrieve team and workflow context so the AI understands your setup

One conversational command. Multiple workflow actions. Zero context switching.

See our CPO Doug give an inside look at how he works with the MCP Server →

Let's say you're fixing a bug. You tell your AI assistant:

"I fixed the pagination issue in the API. Document it, assign to Marco, and move it to Testing."

Behind the scenes, the AI connects to your Atono MCP Server and chains three workflow-aware tools:

• atono_document_bug_fix → adds a plain-English summary of what changed

• atono_update_bug_assignee → assigns Marco

• atono_update_bug_step → moves the bug to Testing

The result: one command, handled safely through Atono. No browser tabs. No copy-paste. No forgetting to document what actually happened.

Watch our senior engineer Lex walk through his real-life workflow with the MCP server →

Create structured stories from conversation:

"Create a story for OAuth GitHub integration. Users should be able to sign in with GitHub, we need to store tokens securely, and handle automatic token refresh. Add it to the Platform team backlog."

Your AI assistant creates a properly structured story with acceptance criteria and adds it to the backlog.

Update work as you complete it:

"I just finished the auth refactor in STORY-445. Move it to Code Review and assign to Taylor."

Your assistant handles the update while you move on to the next task.

Check story details without context-switching:

"Pull up STORY-423 and show me what acceptance criteria still need to be met."

Your assistant retrieves the story and shows you what's left to complete.

See the full list of available tools in our documentation →

Every context switch costs more than time — it costs momentum.

When your developers stop to manage the workflow, their focus breaks. When those updates don't make it back into your planning tool, your visibility breaks. And when coordination becomes a chore, it becomes optional — which means your system of record slowly stops reflecting reality.

The Atono MCP Server closes that loop. It gives AI assistants a structured, secure way to participate in your workflow, so coordination happens automatically — not as an afterthought.

Your developers stay in flow. Your stories stay accurate. Your team moves together again.

When developers update workflows conversationally instead of context-switching:

• Your product team sees reality: Stories reflect actual progress, not what someone remembered to update yesterday. No more “wait, is this actually done?” in standups.

• Your planning gets honest: When updating the board isn’t a chore, your backlog reflects what’s really happening. Velocity metrics become meaningful again. 

• Your scaling doesn’t break: At 20 people, manual updates are annoying. At 50, they’re impossible. Automated coordination scales linearly while your team grows exponentially.

The difference: Teams report shipping materially faster not because they code fast, but because coordination stops being the bottleneck.

We didn't build this as an experiment. We built it because we saw an opportunity.

Our team uses AI tools daily to write and review code. We noticed that while AI made the coding part seamless, updating the workflow still required stepping out of the editor — even if just for a few seconds. It wasn't painful, but it was friction we didn't need.

So we built MCP tools that let AI handle the coordination work in the same conversational flow where the code gets written.

Examples:

• A document_bug_fix tool — because developers forget (or deprioritize) writing summaries after fixing things. Now the AI does it in plain English, automatically.

• Workflow updates scoped to teams — because not every team uses the same "In Progress" or "Testing" step. The tools understand your workflow, not a generic one.

• A single setup that works across AI clients — because your team uses Claude, Cursor, Windsurf, and Copilot. We didn't want to pick winners.

We use this daily. Our developers update workflows conversationally instead of context-switching to the browser. It's not revolutionary — it's just smoother.

The MCP Server runs locally in Docker and enforces your workspace permissions at every interaction. If a developer can't see a story in Atono, they can't access it through AI tools either.

That means developers can't access private team backlogs unless they're members. Can't update bugs they don't have permission to modify. Your permissions travel with your data because they're enforced at the API level, not just in the UI.

We use this daily on our own team. Every AI action respects who can do what—because shipping fast only matters if you can trust what gets shipped.

1. Install Docker Desktop (v27+).

2. Run:

docker pull atonoai/atono-mcp-server:VERSION-NUMBER

3. Add your Atono API key in your AI tool's MCP config.

4. Restart your tool — you'll see Atono listed as a server.

5. Try something simple:

• "Create a story for adding OAuth support for GitHub."

• "Move BUG-312 to Testing and assign to Sarah."

That's it. You've just turned your AI into a member of your workflow.

Need the full setup for your specific tool? View step-by-step instructions →

You don't need an enterprise contract to experiment. Atono is free for up to 25 users, with unlimited issues and all features included.

No demo wall. No sandbox limits. Test it on your real backlog, with your real team, in your real workflow. Not a sanitized demo environment.

This isn't about replacing your tools — it's about connecting them.

Your developers already use AI to code faster. Now, with Atono's MCP Server, your AI can help your team coordinate faster too.

One bridge, built for how teams actually work.