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Now in early access

Ask Axify anything,
from any AI tool.

The Axify MCP server brings live engineering intelligence, DORA, cycle time, AI adoption, team health, into the AI tools your leaders already use. No more dashboard scavenger hunts.

Get early access
Claude
Ask Axify anything…
Axify MCP
Claude Sonnet 4.5
Works with any MCP-compatible AI tool
Claude
At launch
Chat GPT
At launch
Microsoft Copilot
At launch
Gemini
At launch
Any AI Assistant
MCP compatible
What is a MCP?

The open standard for connecting AI to your tools.

The Model Context Protocol is the emerging standard that lets AI clients like Claude securely call into external systems on your behalf, through your permissions, not a back-channel.

The Axify MCP server exposes your engineering data, DORA, flow, AI adoption, team health, as a set of structured tools the AI client can call and reason over. Ask in natural language, get answers from live data.

  • Open protocol
    Any MCP-compatible AI client works the same way.
  • Permission-scoped
    Inherits the access you already have in Axify.
  • Composable tools
    Chain queries to answer questions a single dashboard can't.
Anatomy of a query Live
1
You ask your AI
"How did our payments team do last week?"
2
Your AI calls Axify MCP
get_team_summary(team="payments", range="7d")
3
Axify returns live data
Cycle time 3.2d -12% Deploys 14 +3 AI use 71% +4%
4
Your AI writes the answer
"Payments shipped 14 deploys (up 3), cycle time fell to 3.2 days, and AI tool usage rose to 71%. Strong week, keep an eye on review time…"
Capabilities

Everything Axify shows you, available as a tool call.

V1 ships read-only. Write actions, scheduled workflows, and agentic operations are on the roadmap.

DORA metrics

Deploy frequency, lead time for changes, change failure rate, MTTR, filterable by team and timeframe.

Delivery signals

Cycle time, PR throughput, review time, rework, and quality across every team in your org.

AI Adoption & Impact

Usage, confidence, habit, and consumption broken down by user, team, and tool.

Team & contributor data

List teams, list contributors, get details on any of them, the metadata Claude needs to chain queries.

Packaged summaries

One-call "weekly team summary" and "quarterly board summary" tools that return ready-to-paste narratives.

Composable primitives

Tools chain. Ask compound questions that a single dashboard view can't answer; Claude does the joining.

On the roadmap Write actions (post alerts, update OKRs) · Scheduled briefings to Slack · Cost forecasting · Agentic workflows
How it works

Connect once. Query from anywhere.

Three steps from install to your first answer in your AI tool of choice.

STEP 01

Connect Axify in your AI client

Add the Axify MCP server in Claude, ChatGPT, Gemini, or any MCP-compatible client. One OAuth flow links it to your Axify workspace.

STEP 02

Ask in natural language

Ask any question you'd ask the dashboard, and the harder ones a single view can't answer. Your AI assistant composes tool calls.

STEP 03

Get live answers, scoped to you

Axify pulls fresh data from Jira, GitHub, GitLab, Azure DevOps, and your AI coding tools, scoped to your existing permissions.

See it in action

Ask a question. Get a chart.

Your AI tool calls the Axify MCP server, gets live data, and renders the answer as a visualization, not a wall of JSON.

Claude · with Axify MCP connected
axify.io/mcp
How did the payments team do last week vs the week before? Anything to flag?
CTO
C
Called Axify MCP get_team_summary() ok
Axify Payments — last 7 days vs previous 7
Cycle time
3.2d −11%
Deploys
14 +3
AI adoption
71% +4 pt
Review time
9h +2h

Payments had a strong week. 14 deploys (up 3) and cycle time dropped to 3.2 days.

One yellow flag: review time grew 7h → 9h. Want me to drill into which PRs are getting stuck?

Yes, and compare against the team with the highest AI adoption. Are they faster?
CTO
C
Called Axify MCP compare_teams() ok
Axify Payments vs Platform — last 7 days
Payments Platform
AI adoption
71%
84%
Cycle time
3.2d
2.4d
Review time
9h
5h

Platform leads on AI adoption at 84%, and they're notably faster across the board. Their pattern is shorter PRs reviewed within the day. Worth a sync between the two EMs.

Ask Claude anything about your engineering org…
Demo with sample data. Your queries run against your live Axify workspace.
Use cases

Built around the questions leaders actually ask.

Six scenarios drawn from CTO, VP, and EM workflows we already see in Axify.

CTO
Monday-morning leadership check-in

Open your AI assistant. Ask “How did we do last week?”. Get a narrative summary across DORA, cycle time, and AI adoption, ready to forward to the exec team.

How did we do last week?”
Head of Product
Board-deck prep in one conversation

Draft a board one-pager in your favorite AI assistant. Pull last quarter's DORA trends, AI adoption ROI, and team well-being deltas, and iterate the narrative without ever opening Axify.

Draft my Q3 board one-pager from last quarter's metrics.”
VP Engineering
Compound questions dashboards can't answer

“Which teams have the lowest cycle time but the lowest AI adoption?”, a query that joins three views. Your AI assistant composes the tool calls and returns one answer.

Which teams ship fast but use AI the least?”
Engineer
Self-awareness inside the IDE

“How does my PR review time compare to the team average this sprint?”, answered in your editor, no context switch.

How's my review time vs the team this sprint?”
Engineering Manager
Pre-retro talking points

Ask for the last two-week delta on cycle time, rework, and review time. Walk into retro with talking points already written.

What changed for my team in the last two sprints?”
Roadmap
Roadmap
Scheduled briefings to Slack

Coming after launch: have your AI assistant post your weekly team summary to a channel every Monday at 9am, no human in the loop required.

Every Monday, post last week's summary to #eng-leadership.”
Security & privacy

Your data stays on your terms.

The MCP server inherits the access controls you've already set up. If you can't see a team in Axify, the AI client can't either, no exceptions.

OAuth-based connection
Hosted by Axify. No long-lived API keys to manage or rotate.
Permission-scoped queries
Every tool call respects your existing Axify role and team scope.
Read-only at v1
No writes, no destructive operations, no admin actions until you opt in.
Audit log
Every MCP query is logged in your Axify workspace for review.
Permission flow
MCP request from Claude POST /tools/get_team_summary
OAuth token validated user_id: marc.lvq@…
Scope check: payments team role: vp-eng · allowed
Live query to integrations jira · github · azure
Audit entry written logs.axify.io/q/4f2a
Response returned to client JSON · 0.6s
FAQ

Questions, answered.

Find answers to the most common questions about Axify MCP

  • When will the Axify MCP server be available?

    The Axify MCP server is now available in early access. You can connect it to Claude, ChatGPT, Gemini, and any MCP-compatible AI client today. Reach out to your Axify account team or contact us to get access.

  • Which AI clients are supported?

    Axify MCP is built on the open Model Context Protocol, so it works with any MCP-compatible AI client. Out of the box, that includes Claude, ChatGPT, Gemini, and Microsoft Copilot, with more added as MCP adoption expands. If your AI tool supports MCP, you can plug Axify in.

  • Will it have write access to my Axify workspace?

    No, v1 is strictly read-only. Write actions (creating OKRs, posting alerts, leaving comments), scheduled workflows, and admin operations are on the post-launch roadmap and will be opt-in.

  • How does it handle permissions?

    The MCP server connects via OAuth and inherits your existing Axify role and team scope. If a teammate can't see a team in the Axify dashboard, the AI client they're using can't query that team's data either.

  • What data sources does it pull from?

    The same integrations that already power Axify:  Jira, Azure DevOps, GitHub, GitLab, Bitbucket, and your AI coding tool integrations (Copilot, Cursor, Claude, and Lite LLM). No new connectors required.

  • Does it cost extra on top of my Axify subscription?

    Pricing for the MCP server is being finalized. We'll share details with waitlist members ahead of public launch.

  • Is it self-hostable?

    At launch, the MCP server is hosted by Axify (so OAuth and updates are handled for you). We're evaluating self-hosted deployment as a follow-up. Let us know if it's a requirement for your org.

Now in early access

Ask Axify anything, from your AI tools.

Early access is now open. Connect Axify to Claude, ChatGPT, Gemini, or any MCP-compatible AI tool and start querying your live engineering data.

Get early access
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