MCP Recipe for Code Review Time Analytics.
Review bottlenecks detected, unreviewed PRs surfaced, reviewer workload balanced, team velocity measured , fix your code review process with data
Works with every AI agent you already use
…and any MCP-compatible client
Waiting for input…
Gemini How It Works
Your AI agent reads GitHub: 14 open pull requests across 5 repositories. 4 have been waiting for review for more than 24 hours.
PR #198 has been open for 72 hours with no reviewer assigned , it is blocking a feature launch. The agent analyzes review patterns: @maria reviewed 8 PRs this week, @carlos reviewed 2, @james reviewed 1.
Review load is unbalanced. Average time from PR open to first review: 14.3 hours. Average time from approval to merge: 2.1 hours , the review-to-merge lag is fine, it is the wait-for-review that is slow.
The agent ingests these metrics into Axiom: per-reviewer load, per-repo review latency, PR aging distribution. Axiom query shows the trend: review latency has increased 35% over the last month.
Cause: team grew from 4 to 6 engineers but reviewer pool stayed at 3. It posts to #engineering: 'Review Bottleneck Report , 4 PRs waiting > 24h.
Review latency: 14.3h avg (+35% MoM). Reviewer load: @maria 8 PRs (overloaded), @carlos 2, @james 1. Fix: Add @sarah and @alex to reviewer rotation.
Stale PR: #198 (72h, no reviewer, blocking feature launch).'
MCP Server Orchestration: 3 MCP Servers, one intelligent agent
Connect GitHub, Axiom and Discord MCP servers so your AI agent analyzes your pull request review process, ingests review metrics into Axiom for trend analysis, identifies bottlenecks (PRs waiting > 24h, unbalanced reviewer load, review-to-merge lag), and delivers actionable insights to your Discord channel. Engineering teams where code review is the bottleneck get data-driven process improvements. No more 'reviews feel slow' , you see exactly where reviews stall, who is overloaded, and which PRs are aging. One prompt and your review process is visible.
Github
triggerReads pull requests, review assignments, approval status and timing
list_pull_requests get_repository_details list_user_repositories search_github_repositories Axiom
actionIngests review metrics and runs trend queries over time
ingest_data run_query list_datasets create_dataset Discord
actionPosts review bottleneck reports and stale PR alerts
create_message list_guild_channels Run This Automation Today
Connect Claude, ChatGPT, Cursor, or any AI agent to the Vinkius catalog and run this automation in minutes.
Build Your Own MCP
Turn any internal API into an MCP server. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Connect & Automate
The 3 servers this recipe uses are ready in the catalog. Connect them once, paste a prompt, and your AI runs the full workflow.
- Github, Axiom & Discord ready in the catalog right now
- Add more from 4,700+ servers whenever you need
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers and recipes added every week
Superpowers you didn't know your AI had
The Vinkius catalog gives your agent access to 4,700+ MCP servers and the intelligence to combine them. Imagine never logging into another dashboard. Your AI handles the work across every tool, in one conversation. That's what this infrastructure was built for.
Cross-Platform Intelligence
Your agent doesn't just connect to tools. It understands the relationships between them. Data flows where it needs to go, automatically, with full context preserved across every platform.
Contextual Reasoning
Every decision your agent makes considers the full picture. It reads CRM data, checks calendars, reviews conversation history, and acts on everything at once. Not step by step. All at once.
Productivity at Scale
What used to take 45 minutes across five different dashboards now takes one sentence. Your agent runs the entire workflow end to end while you focus on decisions that actually matter.
Zero-Config Reliability
No API keys to paste. No webhooks to configure. No YAML to debug. Connect your MCP servers once, and your agent handles the rest. Every time, without intervention.
Made for
exactly this
Your AI agent taps into the entire Vinkius MCP catalog to handle these for you. You describe what you need. It does the rest.
Engineering teams where code review is the development bottleneck and managers need data to justify process changes
Tech leads who want to rebalance reviewer workload across the team without guessing who is overloaded
Engineering managers tracking pull request velocity as a proxy for team health and delivery speed
Teams scaling from 4 to 10+ engineers who need to formalize their review process before it breaks
Frequently Asked Questions About This MCP Server Orchestration
Which MCP servers do I need for this workflow?
Three: GitHub, Axiom and Discord. Connect all three to your AI client.
Does this work with Claude Desktop, Cursor or Windsurf?
Yes. Any AI client that supports the Model Context Protocol works , Claude Desktop, Cursor, Windsurf, Cline and others.
Can I use GitLab instead of GitHub?
Yes. Replace the GitHub MCP server with the GitLab MCP server. The agent reads merge request review data instead.
What data gets stored in Axiom?
Per-PR metrics: open time, first review time, approval time, merge time, reviewer, author, repo, labels. No code content is stored.
Can I use Google Sheets instead of Axiom?
Yes. Axiom provides better time-series querying for trends, but Google Sheets works for basic tracking. Replace the Axiom MCP with Google Sheets.
How does this help with review quality, not just speed?
The agent tracks review comments per PR and approval-without-comments rates. A reviewer who approves 10 PRs with zero comments may not be reviewing thoroughly.
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MCP servers used in this workflow
GitHub
GitHub MCP Server manages repositories, tracks issues, and searches code via AI agents. Connect your GitHub account to your preferred AI client and automate core developer workflows—listing repos, getting file contents, or creating new issues—all from a natural conversation. Manage your entire software development lifecycle without leaving your chat window.
Axiom
Axiom. Connect your AI client to Axiom to manage logs and observability data. Ingest JSON, NDJSON, or CSV data and run complex Axiom Processing Language (APL) queries to analyze logs in real-time. You can manage datasets, create monitors, and track system errors directly through natural conversation with your agent.
Discord
Discord MCP Server gives your AI agent full control over Discord communities. You can list channels, manage members, send messages with Markdown, and run moderation commands—all without leaving your chat client. It lets your agent read channel history, audit server metadata, and delete messages or channels instantly.