
Keypup Engineering Analytics
io.keypupPublished by keypup.ioOfficial publisherv1.0.1Updated Oct 25, 2026
Generate answers & visualizations from your engineering data to track software development health.
Overview
Keypup's remote MCP server lets an AI assistant query your engineering analytics data in plain language and build read-only reports on delivery, quality andteam
- What you can do
- List the companies (teams) you belong to and browse available datasets and their fields, including custom fields and formula operators with documentation. Run aggregated reporting queries over issues, pull requests, activity events, comments, commits and reviews, with metrics, group-by dimensions, filters, sorting and pagination. Turn a plain-language question into a structured query and then execute it. Summarize results such as merge throughput, cycle time, review turnaround, bug open/close rates and reviewer workload.
- When to use it
- Use it when you want to ask questions about software delivery, code review and team workload without writing queries, or when you want an assistant to draft and run Keypup reports for you. It suits engineering managers and team leads exploring Keypup data conversationally rather than through the GraphQL API.
- Requirements
- A remote streamable HTTP endpoint; no local package. Authentication is required: OAuth 2.1 with dynamic client registration (browser sign-in) is recommended, or a Keypup API token sent in the Authorization header with the Bearer prefix. Token scopes viz:reports:read and hq:companies:read are recommended. A Keypup account with membership in the relevant companies is needed.
Installation
In SourceWeft
- Open Keypup Engineering Analytics in the dashboard and add it to a workspace.
- Connect the required credentials. They are stored privately and never shown on public pages.
- Enable the server for the chats that should use its tools.
Web executable via Streamable HTTP. Remote servers run from the web runtime once configured in a workspace.
Use with an agent
Paste this prompt into Claude Code, Codex, Cursor or another agent — or into SourceWeft chat. The agent reads this server's install guide, shows you its publisher, endpoint and sign-in, and adds it to your client once you agree. Keys never go into a command.
Read https://sourceweft.com/mcp/io.keypup%2Fengineering-analytics/install.md and connect the MCP server it describes to my client. Before changing any config, show me its publisher, endpoint and sign-in method and wait for my OK. Never put an API key in a command or in a file I would commit; ask me where it should come from.Other MCP clients
Add this to your client's mcpServers config. The client completes OAuth sign-in on first connect.
{
"mcpServers": {
"keypup-engineering-analytics": {
"type": "http",
"url": "https://hq.keypup.io/mcp"
}
}
}README
Keypup MCP Server
[Install in VS Code] [Install in Cursor] [Add to Kiro]
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:
- List your companies
- Pick the relevant dataset
- Look up the fields it needs
- Build the query
- Run it
- 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 one issue?"
- "How many PRs were merged without a review?"
Workload & collaboration
- "Who are our most active reviewers this month?"
- "How is work distributed across the team right now?"
- "How many comments do our pull requests receive on average?"
Open-ended exploration
- "Summarize our engineering activity over the last month."
- "Compare open vs. closed issues over time."
- "What labels are most common on our issues?"
You can refine iteratively: "now break that down by repository", "restrict it to the backend team", and so on.
Setup
Endpoint
The server uses the streamable HTTP transport and supports two authentication methods:
- OAuth 2.1 (recommended) — the client registers itself dynamically and you authorize access through your browser. No token to copy or store.
- API token — a Keypup API token passed in the
Authorizationheader. Use this when your client does not support OAuth dynamic client registration.
Option 1 — OAuth (recommended)
If your client supports OAuth 2.1 with dynamic client registration (e.g. via a Client ID Metadata Document), point it at the endpoint with no credentials:
On first connection the client opens a browser window to sign in to Keypup and
authorize access. There is no token to generate, copy, or rotate — this is the
configuration shipped in this repo's .mcp.json.
If your client does not open an authorization prompt (or fails to register), it likely does not support dynamic client registration yet. Use the API token method below instead.
Option 2 — API token
For clients without OAuth dynamic client registration support, add a keypup
server entry pointing at the endpoint, with your API token in the Authorization
header:
Notes:
- Replace
my_keypup_api_tokenwith the token you generate (see below). Keep theBearerprefix. - The server name (
keypup) is arbitrary — name it however you like.
Once saved, restart or reconnect the MCP server in your client. The Keypup tools should appear in the client's tool list, and you can start asking questions.
Generating an API token
Only needed for Option 2 (API token). If you authenticate via OAuth, skip this section.
The MCP server authenticates with the same API tokens used by the GraphQL API.
- Go to Settings → Account and open the API Token tab.
- Click Create token.
- Set a descriptive name (e.g., "MCP - Claude Desktop") and an expiration. Setting an expiration is strongly recommended.
- Select the token scopes. The minimal recommended scopes for the MCP server are:
- Reports read (
viz:reports:read) — required to explore datasets/fields and run reporting queries. - Companies read (
hq:companies:read) — required to list the companies you belong to and resolve the company ID used in queries.
- Reports read (
- Click Create. The token is shown only once and can be copied for one minute — copy it into your MCP client configuration right away.
Following the principle of least privilege, grant only the scopes you need. The two scopes above are enough to use the full analytics capabilities of the MCP server.
The token grants the same level of data access you already have in the Keypup user interface — queries are automatically scoped to the companies you are a member of.
Security
- All Keypup MCP tools are read-only — they never modify your data.
- Queries are automatically scoped to the companies you belong to (data isolation).
- Treat your API token like a password. Anyone holding it can query your Keypup data within the granted scopes.
- Prefer short-lived tokens with an expiration, and rotate them periodically.
- Keep tokens out of shared or version-controlled files. Store them in your MCP client's local configuration only.
Resources
Source: README.md at commit 03c8bc2
Tools
0Version history
1- v1.0.1LatestSep 16, 2026
Score
75% of the applicable checks met
How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.
- Reached in the last 30 daysRequired25 pts
- Auth declaredRequired10 pts
- Tools listed15 pts
Answer tools/list without a credential, or ship a package that starts in a sandbox without one.
- DescriptionRequired15 pts
- Logo10 pts
- Documentation15 pts
- License10 pts
Declare a license in the repository or the registry entry.