github · MCP Server

GitHub Data Meets AI — Powered by Improvado MCP

Connect GitHub to Claude, Cursor, and other AI agents through Improvado's MCP server. Ask questions about pull requests, review cycles, and team velocity in plain English. No more digging through dashboards or writing custom scripts.

46K+ metrics ·Read & Write access ·500+ platforms ·<60s setup

Read

Ask GitHub anything

Your AI agent becomes a direct line to repository data. Check PR status, review bottlenecks, issue backlogs, and commit history without leaving your conversation. Engineering metrics on demand.

Example prompts

"Open PRs waiting for review over 3 days"

8 min → 15 sec

"Top repos by commits this sprint + top contributors"

15 min → 20 sec

"P0 issues that missed their milestone deadline"

5 min → 10 sec

Works with Claude ChatGPT Cursor +5

Write

Take action without switching tabs

Create issues, update PR labels, assign reviewers, and manage milestones directly through your AI agent. The context is already there. Just tell it what needs to happen.

Example prompts

"Create issue: investigate /auth timeout errors in api-gateway"

3 min → 10 sec

"Label all open PRs touching auth code as needs-security-review"

12 min → 20 sec

"Assign @mike and @priya as reviewers on PR #847"

2 min → 5 sec

Every action logged · Fully reversible · SOC 2 certified

Monitor

Set up alerts that actually matter

Stop checking GitHub obsessively. Configure your AI agent to notify you when PRs go stale, critical issues pile up, or CI/CD pipelines fail. Context-aware alerts based on your team's actual workflow.

Example prompts

"Alert when any PR waits for review over 24 hours"

Manual → auto

"Flag repos with 5+ open P0 bugs at once"

Manual → auto

"Notify when CI fails on any production service repo"

Manual → auto

Alerts sent to Slack, email, or your AI agent

Full cycle

From question to action to insight

Your AI agent doesn't just surface data — it acts. Adjust pricing, update product descriptions, manage inventory, apply discounts — all through natural language. The MCP server translates intent into API operations.

Every phase runs through the same MCP connection. One protocol, all platforms, full governance. No switching between tools.

Ideate

Launch

Measure

Analyze

Report

Iterate

One conversation. All six phases. Every platform.

The daily grind

Common problems. Direct answers.

Challenge 1

Cross-repo visibility is a nightmare

The problem

You manage 15 microservices across different repos. Finding out which ones have pending security updates means opening tabs, checking branches, and cross-referencing Dependabot alerts. By the time you've mapped it all out, you've lost 45 minutes.

How MCP solves it

Ask your AI agent for a consolidated view. It pulls data across all repos you specify, surfaces patterns, and even suggests which updates to prioritize based on dependency graphs and recent commit activity.

Try asking

Which of our production repos have open Dependabot PRs for critical vulnerabilities?

Answer in seconds

All data sources, one query

Challenge 2

PR review cycles kill velocity

The problem

Your team ships fast, but PRs sit idle. You don't know which ones are actually blocked vs. which just haven't been seen. Checking each PR's timeline, reviewer status, and comment threads takes forever. Sprint velocity suffers because of invisible friction.

How MCP solves it

Get instant visibility into review bottlenecks. See which PRs are waiting on specific people, which have unresolved conversations, and which are approved but not merged. Identify patterns like reviewers who are overloaded or code areas that always slow down.

Try asking

Show me PRs blocked on review for >48 hours, grouped by which reviewer is holding them up

Full detail preserved

No data loss on export

Challenge 3

Engineering metrics live in different tools

The problem

Cycle time comes from GitHub. Deployment frequency lives in your CI/CD tool. Incident data sits in PagerDuty. Building a DORA metrics dashboard means API calls, data pipelines, and someone maintaining scripts. It's never current and breaks constantly.

How MCP solves it

Query GitHub metrics alongside your other engineering data through one MCP connection. Combine PR merge times with deployment data and incident frequency. Your AI agent joins the dots across tools without you building integration plumbing.

Try asking

What's our average time from PR open to merge over the last 30 days, broken down by repo?

Unified data model

Compare anything side by side

👥 Teams

One Framework. Five Roles. Zero Setup.

Same MCP connection, different workflows for every team member. Each role asks in natural language — the MCP server handles the complexity (rate limits, auth, schema normalization, governance) behind the scenes.

Agency CEO

Portfolio health. Client risk. Revenue signals.

Media Strategist

70% strategy, not 70% ops. Auto campaign QA.

Marketing Analyst

Zero wrangling. Cross-platform. AI narratives.

Account Manager

QBR decks auto-generated. Call prep in 30s.

Creative Director

Performance-to-brief. Predict winners before spend.

FAQ

Common questions

Which GitHub data can I access through MCP?

Pull requests, issues, commits, branches, code reviews, comments, milestones, projects, Dependabot alerts, Actions workflows, and repository metadata. Basically everything you'd access through GitHub's API, but queryable in natural language.

Does this work with GitHub Enterprise?

Yes. Point the MCP server at your GitHub Enterprise instance during setup. Works with both cloud and self-hosted deployments — all through Improvado's hosted MCP server.

Can I write back to GitHub or just read data?

Both. Create and update issues, manage PR labels and reviewers, update milestones, add comments, and trigger workflows. Read and write permissions are controlled by your GitHub token scope.

How does this compare to building GitHub API scripts?

You skip all the boilerplate. No authentication handling, pagination logic, rate limit management, or response parsing. Just ask questions. The MCP server handles API complexity and your AI agent structures the results — all through Improvado's hosted MCP server.

What AI agents work with this?

Any MCP-compatible client. That includes Claude Desktop, Cursor, and other editors or tools that support the Model Context Protocol. More agents are adding MCP support regularly — all through Improvado's hosted MCP server.

How long does setup take?

Under 5 minutes. Add the MCP server to your AI agent, authenticate with a GitHub personal access token, and start querying. No data pipelines to configure or infrastructure to deploy — all through Improvado's hosted MCP server.

Stop Reporting. Start Executing.

Connect your data to an AI agent in under 60 seconds. The closed loop starts with one conversation.

SOC 2 Type II GDPR 500+ Platforms