Keypup MCP Server

Ask your Keypup engineering data in plain language to track delivery, quality and team workload.

The Keypup MCP (Model Context Protocol) server plugs your Keypup engineering analytics directly into any MCP-compatible AI assistant — Claude, Cursor, Kiro, ChatGPT Desktop, Gemini CLI, VS Code, and others.

Once connected, you ask questions about your engineering activity in plain language and the AI builds and runs the underlying reporting queries for you. No query language to learn, no code to write.

Unlike the GraphQL API, which is designed for developers writing application code, the MCP server is designed to be driven conversationally by an AI on your behalf.

Beta: The Keypup MCP server is currently in beta. The exposed capabilities are continuously expanded during this phase. Feedback and use cases are welcome via the in-app chat.

What it can do

The server exposes a focused set of read-only tools that let an AI explore and query your Keypup data:

• Discover your companies — list the companies (teams) you belong to.
• Explore datasets — browse the available datasets and the fields each one exposes.
• Discover formula operators — list the functions and operators available when building metrics, dimensions, and filters.
• Run reporting queries — execute aggregated queries against any dataset, with metrics, dimensions (group-by), filters, sorting, and pagination.
• Generate a query from natural language — turn a plain-language request into a ready-to-run structured query that the AI can then execute.

The AI orchestrates these tools automatically. A typical flow:

1. List your companies
2. Pick the relevant dataset
3. Look up the fields it needs
4. Build the query
5. Run it
6. Summarize the results

You only ask the question.

Tools

Datasets

The reporting engine runs over the following datasets (facts):

• ISSUES_PULL_REQUESTS — issues and pull requests.
• ACTIVITY_EVENTS — transitions on issues/PRs (status changes, assignments, work logged, open/close/reopen).
• COMMENTS — comments on issues, PRs and reviews.
• COMMITS — commits attached to pull requests.
• REVIEWS — pull request reviews.

Example questions

Delivery & throughput

• "How many pull requests did we merge each month over the last 6 months?"
• "What's our weekly issue closing rate this quarter?"
• "How many commits were made per author last month?"

Cycle time & performance

• "What's the average time between PR creation and merge over the last 12 weeks?"
• "Show me the review turnaround time trend for the last 3 months."
• "Which repositories have the slowest cycle time?"

Quality & process

• "How many bugs were raised vs. closed each week this quarter?"
• "What proportion of our pull requests resolve at least

... View full README on GitHub