PAPI — Persistent Agentic Planning Intelligence
Run structured planning cycles—tasks, strategy reviews, and handoffs to implementation—so your agent does not drift across a solo project.
Overview
PAPI is a MCP server for the Validate phase that structures AI project planning with cycles, tasks, strategy reviews, and build handoffs.
What is this MCP server?
- Persistent Agentic Planning Intelligence (PAPI) via @papi-ai/server stdio (npx, v0.7.12)
- Structured cycles, tasks, and strategy reviews for long-running agent projects
- Build handoffs from planning state into implementation work
- UI and docs at https://getpapi.ai; server repo cathalos92/papi-ui on GitHub
- npm-based stdio MCP suitable for Claude Code and Cursor style workflows
- MCP server version 0.7.12; npm package @papi-ai/server
- Stdio transport with npx runtimeHint
- Four planning pillars in description: cycles, tasks, strategy reviews, build handoffs
What problem does it solve?
Solo builders lose decisions and task order across agent sessions, so scope creep and restart-heavy coding waste validation time.
Who is it for?
Indie developers running multi-week agent-assisted projects who need structured PM state beyond a single chat thread.
Skip if: One-session bugfixes or builders who already run a rigid Superpowers stack and do not want another planning layer.
What do I get? / Deliverables
You get persistent cycles and tasks with strategy reviews and clear handoffs so your agent continues from an approved plan into build work.
- Structured project cycles and task lists visible to the agent
- Documented strategy reviews before major build commits
- Explicit build handoffs from planning state to implementation tasks
Recommended MCP Servers
Journey fit
Persistent planning intelligence matters most when you are scoping what to build and proving the idea is worth a full implementation push. PAPI encodes scope, cycles, and strategy reviews—the validate shelf—while explicitly supporting build handoffs into execution.
How it compares
Persistent agentic planning MCP, not a deployment or monitoring integration.
Common Questions / FAQ
Who is PAPI for?
Solo and small-team builders using Claude Code or similar agents who need durable cycles, tasks, and strategy reviews across many sessions.
When should I use PAPI?
Use it when validating and scoping an MVP, then keep it through build handoffs whenever the agent must track what is in-cycle versus deferred.
How do I add PAPI to my agent?
Configure stdio MCP to run @papi-ai/server via npx per getpapi.ai instructions, version 0.7.12, and connect your client before starting planning cycles.