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@papi-ai/server
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PAPI MCP server — AI-powered sprint planning, build execution, and strategy review for software projects
AI-powered sprint planning, build execution, and strategy review for software projects. PAPI is an MCP (Model Context Protocol) server that connects to Claude Code to manage your project's planning lifecycle.
npm install -g @papi/server
git clone https://github.com/cathalos92/papi-ui.git
cd papi-ui
npm install
npm run mcp-build
The server bundles @papi-ai/adapter-md at build time — no separate adapter install needed.
Add PAPI to your project's .mcp.json file:
{
"mcpServers": {
"papi": {
"command": "npx",
"args": ["papi-server", "--project", "/path/to/your/project"],
"env": {
"PAPI_ADAPTER": "pg",
"DATABASE_URL": "postgresql://...",
"PAPI_PROJECT_ID": "your-project-uuid"
}
}
}
}
{
"mcpServers": {
"papi": {
"command": "node",
"args": ["/path/to/papi-ui/packages/server/dist/index.js", "--project", "/path/to/your/project"],
"env": {
"PAPI_ADAPTER": "pg",
"DATABASE_URL": "postgresql://...",
"PAPI_PROJECT_ID": "your-project-uuid"
}
}
}
}
For local-only usage with markdown files (no database), omit the PAPI_ADAPTER, DATABASE_URL, and PAPI_PROJECT_ID variables — the server defaults to the markdown adapter.
| Variable | Required | Default | Description |
|---|---|---|---|
PAPI_PROJECT_DIR | Yes* | — | Project root directory (alternative to --project flag) |
PAPI_ADAPTER | No | md | Storage adapter: md (local markdown files) or pg (PostgreSQL/Supabase) |
DATABASE_URL | For pg | — | PostgreSQL connection string (required when PAPI_ADAPTER=pg) |
PAPI_PROJECT_ID | For pg | — | Project UUID in the database (required when PAPI_ADAPTER=pg) |
PAPI_API_KEY | No** | — | Anthropic API key for AI-powered commands (plan, strategy, setup) |
PAPI_AUTO_COMMIT | No | true | Auto-commit .papi/ changes after plans and builds |
PAPI_AUTO_PR | No | true | Auto-create pull requests after completed builds |
PAPI_BASE_BRANCH | No | main | Base branch for PR creation and plan sync (pull/push) |
PAPI_SLACK_WEBHOOK_URL | No | — | Slack incoming webhook URL for sprint notifications |
* Either PAPI_PROJECT_DIR or --project flag is required.
** Not required when using the prepare/apply pattern (default). Only needed if calling the Anthropic API directly from the server.
Once configured in Claude Code, initialise your project:
setup creates a .papi/ directory with your Product Brief and planning filesplan runs a sprint planning cycle, generates BUILD HANDOFFs for recommended tasksbuild list shows available tasks, build execute <task-id> starts implementationbuild execute <task-id> again with completion detailsplan again when all sprint tasks are done| Command | Description | Requires API Key |
|---|---|---|
setup | Initialise a new PAPI project or regenerate Product Brief | Yes |
plan | Run sprint planning — generates BUILD HANDOFFs for 1-5 tasks | Yes |
build | List, describe, and execute build tasks | No |
board | View sprint board, filter tasks, deprioritise stale work | No |
strategy | Run strategy review or apply a strategic change | Yes |
idea | Capture a backlog idea without interrupting the current sprint | No |
health | Sprint health dashboard — cadence, board stats, next action | No |
release | Cut a versioned release with git tag and changelog | No |
PAPI manages your project through markdown files in a .papi/ directory:
PRODUCT_BRIEF.md — Your project's vision, users, and roadmapPLANNING_LOG.md — Sprint health, active decisions, sprint historySPRINT_BOARD.md — All tasks with status, priority, and build handoffsBUILD_REPORTS.md — Post-build reports tracking effort and discoveriesThe AI planner reads this context each sprint to make informed recommendations that compound over time — sprint 20 is smarter than sprint 1 because it has 19 sprints of history to learn from.
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FAQs
PAPI MCP server — AI-powered sprint planning, build execution, and strategy review for software projects
The npm package @papi-ai/server receives a total of 768 weekly downloads. As such, @papi-ai/server popularity was classified as not popular.
We found that @papi-ai/server demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.

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