Planning
Your coding agents get the whole system, and a plan to build from.
Add ProdE's MCP server to Claude Code or Cursor once. Every session starts with the service map, dependencies, generated docs and search across every repo. Planning turns an idea into a spec grounded in your codebase and tasks your agents can execute.
The whole system, not one repo.
Claude Code, Cursor, Codex, Copilot, Kiro, OpenCode, Devin Desktop, Gemini CLI. Any MCP client.Claude Code gets the whole system over MCP
Coding agents read the system map before they touch code, not just the repo they are open in. No CLAUDE.md files to keep up to date.
A read-only shell over every repo
Agents search every repo, Jira and Confluence inside ProdE instead of cloning them. Indexed once, so the agent looks things up instead of re-reading the codebase every session.
Decisions logged back
Agents pull the approved requirement and acceptance criteria, build against them, and record decisions and constraints on the requirement as they go.
"Without ProdE, every session we'd re-index the project and it kept taking lots of tokens. Now it's indexed in one place and we just run our queries."
From idea to agent-ready plan.
Your PMs and engineers make decisions, ProdE does the grunt work.
Everything before coding begins
Every PM starts working like your best staff PM, every engineer like your best staff engineer.Living specs & collaborative PRDs
Eliminate spec driftGenerate PRDs and requirements grounded in your codebase. Specs stay aligned as code evolves. Teams review, comment, and align in one place instead of scattered docs.
Change impact analysis
See the blast radius before codingSee what a change touches before writing code. Cross-service dependency mapping surfaces affected endpoints, consumers, and edge cases. Self-serve "what breaks if we change X?" without pulling a senior engineer.
MCP server for coding agents
Guardrails + context for AI agentsGive Cursor, Claude Code, Copilot, and Devin Desktop direct access to your specs, requirements, and architectural context via MCP. Agents work from approved plans with clear boundaries, not guesswork.
Requirements arrive at engineering already checked, from a PRD or a Jira epic.
PMs refine PRDs in ProdE. It checks each feature against the existing system, flags what it will affect, adds the team's development practices, and breaks the work into tasks that engineers pull into VS Code over MCP.
2,400 product questions answered against the code in seven months. The edge cases get found in planning, not in QA.
"ProdE is one of the most important tools in Spinny's tool chain."
FAQ
How does ProdE relate to Jira, Linear, or ClickUp?
Software delivery has outgrown task lists. Your ticketing tool tracks work items; ProdE grounds those work items in what your system actually does. It's system-aware planning that understands your codebase, maps dependencies across services, and keeps specs aligned with reality as code evolves. They work together. ProdE connects to Jira and Confluence, and adds the codebase intelligence those tools were never designed to have.
How does ProdE fit spec-driven development and tools like GitHub Spec Kit?
Spec-driven development works when the spec is grounded in your actual system. ProdE owns the layer before the coding agent: it researches your codebase, surfaces the decisions only a human can make, and produces agent-ready specs you can hand to any agent or spec-driven workflow.
Will ProdE reduce our token costs?
Rework is the expensive part of agentic development, in time and tokens. When agents work from approved, complete specs instead of guessing, iterations drop. As DIRO's CEO put it: 'Our token cost crashed.'
Ready to see ProdE on your codebase?
Setup in 2 minutes. See results on your own repos.