MCP Servers for Sprint Report Generation.
Sprint reports that write themselves , issues, PRs and velocity stats in one sheet
Works with every AI agent you already use
…and any MCP-compatible client
Waiting for input…
Gemini How It Works
Your AI agent reads the current or most recent cycle in Linear and pulls every issue marked as done. For each issue, it jumps to GitHub and searches for the pull request by branch name or issue reference.
It grabs the PR merge date, review count and lines changed. Then it calculates the cycle time and writes everything into a Google Sheet: issue title, assignee, priority, linked PR, cycle time, lines changed.
The bottom row shows totals , issues closed, average cycle time and top contributor by volume.
MCP Server Orchestration: 3 MCP Servers, one intelligent agent
Connect Linear, GitHub and Google Sheets MCP servers so your AI agent builds sprint reports without anyone touching a spreadsheet. It pulls completed issues from Linear, matches each one to its GitHub pull request, calculates cycle times, and writes the full report to a Google Sheet.
Linear
triggerReads sprint issues, cycle data and completion status
list_issues list_cycles get_issue Github
actionFinds matching pull requests and review timelines
list_pull_requests get_repository_details search_github_code Google Sheets
actionWrites the formatted sprint report to a shared spreadsheet
append_rows get_spreadsheet update_cells 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.
- Linear, Github & Google Sheets 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 managers who spend Friday afternoons building sprint reports from Linear and GitHub data manually
Scrum masters running retrospectives who need actual cycle-time metrics instead of story point estimates
CTOs reporting engineering velocity to the board with real PR-backed data instead of ticket-count vanity metrics
Remote teams that need a shared sprint dashboard without buying another analytics tool
Frequently Asked Questions About This MCP Server Orchestration
Which MCP servers do I need for this workflow?
Three: Linear, GitHub and Google Sheets. Connect all three to your AI client before running any prompt from this page.
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. Connect the MCP servers and paste a prompt.
How does the agent match Linear issues to GitHub PRs?
It searches by branch name pattern (feature/LIN-XXX) and by commit messages containing the Linear issue ID. If your team uses a different convention, specify it in the prompt and the agent adapts.
Can I run this for multiple teams at once?
Yes. Ask for all completed issues across all teams in the current cycle and the agent pulls everything. It creates a separate tab per team in the Google Sheet if you ask for it.
Is my project data secure?
MCP servers authenticate through OAuth or API tokens. Your agent only sees projects, repositories and sheets you have granted access to. Vinkius does not store your data.
What if we use story points instead of cycle time?
The agent reads whatever fields your Linear issues have. If you track story points, ask for those in your prompt. The report format adjusts to whatever metrics matter to your team.
Find API Vulnerabilities First Using MCP
Your OpenAPI spec has 14 security findings and 3 match active HackerOne reports , your agent creates the tickets before the bounty payout
Find Codebase Duplications Using MCP Servers
Your codebase has 4 different implementations of date formatting, 3 versions of the retry logic, and 2 competing validation libraries , but nobody knows because grep only finds exact matches and these duplicates are semantic
How MCP Servers Auto-Triage Bug Reports
New bugs detected, severity classified, sprint tickets created, team notified , triage your backlog without a standup
MCP Recipe to Fix Production Crashes Faster
Your app crashed 847 times yesterday and the error report sits in Honeybadger while your Linear board has no idea , the engineer who wrote the broken code merged a different PR today
MCP Recipe to Kill Codebase Bloat
Codebase audited, bloat identified, requirements questioned, lean tickets created , kill architectural complexity before it ships
MCP Servers for Multi-Client Sprint Management
Your dev team tracks their work in Linear but the PM reports to clients in ClickUp , which means every sprint update is manually transcribed between two tools, and by the time the client sees it in ClickUp the data is already outdated
MCP servers used in this workflow
Linear
Linear lets your AI client read, write, and manage issues directly inside Linear—no tab switching needed. You can list all teams, search for specific bugs, create new tasks with defined priorities, or add comments right from your IDE. It gives your agent full control over project metadata, allowing you to check sprint progress, view project scope, and audit issue status using natural conversation.
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.
Google Sheets
Google Sheets MCP Server lets your AI client read, write, and manage data directly in Google Sheets. Use conversational commands to pull data from specific ranges, append new rows, or structure entire spreadsheets. It acts as an analyst, letting you manipulate complex data without opening the GUI or writing formulas.