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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.
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.
Your AI agent reads harmonized data across 500+ platforms. "Cost" in Google Ads and "spend" in Meta Ads resolve to the same field automatically.
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.
250+ governance rules enforce naming conventions, budget limits, and KPI thresholds. SOC 2 Type II certified.
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.
Automated weekly reports, anomaly flagging, and budget alerts — all from a single conversation. No more morning check-ins across 5 dashboards.
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.
Every phase runs through the same MCP connection. One protocol, all platforms, full governance. No switching between tools.
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.
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.
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.
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.
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.
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.
Same MCP connection, different workflows for every team member. Agency CEOs get portfolio health. Media Strategists get campaign QA. Analysts get cross-platform reports. Account Managers get auto-generated QBR decks. Creative Directors get performance-based briefs.
Each role asks in natural language. The MCP server handles the complexity — rate limits, auth, schema normalization, governance — behind the scenes.
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.
Yes. Point the MCP server at your GitHub Enterprise instance during setup. Works with both cloud and self-hosted deployments.
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.
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.
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.
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.
Connect your data to an AI agent in under 60 seconds. The closed loop starts with one conversation.
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