MK Plan Master

AI 規劃大師 — ideas in, prioritized plans out. Spec drafts that hand straight to mk-spec-master.

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Idea triage + RICE scoring + quarterly roadmap + spec-draft bridge — over MCP. Reads from Linear / JIRA / Notion / Markdown, hands the generated spec draft directly to mk-spec-master.parse_spec, and remembers every decision so the same idea never bounces back unexplained.

🟢 Alpha — v0.1. 15 tools + 4 adapters + 77 tests passing. Full design in docs/prd.md. Walkthrough using a real dogfood case in docs/walkthrough.md.

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Why this exists

With mk-plan-master shipped, the AI-driven dev pipeline now looks like this:

Idea  →  Plan       →  Spec        →  Code     →  Test      →  Coverage    →  Coach
         mk-plan       mk-spec        your IDE     mk-qa       mk-spec        both

Until v0.1 the upstream slot was a ??? — mk-spec-master could parse a spec, mk-qa-master could run tests, but nobody had built the piece that turns a pile of 30–200 raw ideas (chat snippets, customer calls, URLs, gut hunches) into a prioritized, RICE-scored backlog and emits a spec draft that drops straight into mk-spec-master.parse_spec(raw_text=...) — no manual reformatting, no copy-paste fragility.

mk-plan-master is that piece. The mk-* family is now whole:

• mk-plan-master — ideas in, prioritized plans + spec drafts out (this repo)
• mk-spec-master — specs in, scenarios out, coverage matrix
• mk-qa-master — scenarios in, runnable tests out (pytest / Jest / Cypress / Go test / Maestro)

It's also the planning MCP that measures its own decision quality over time — history snapshots, decision signatures (ghost initiatives / score whiplash / orphan OKRs), and tool-usage telemetry. The mk-spec-master v0.4 self-reinforcement layer, applied one step upstream.

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The family loop

   ┌─────────┐      ┌──────────┐      ┌─────────┐      ┌─────────┐      ┌──────────┐      ┌─────────┐
   │  Idea   │ ───> │   Plan   │ ───> │  Spec   │ ───> │  Code   │ ───> │   Test   │ ───> │ Coverage│
   │ (chat,  │      │ mk-plan- │      │ mk-spec-│      │ your IDE│      │ mk-qa-   │      │ mk-spec-│
   │ URL,    │      │ master   │      │ master  │      │ (Claude │      │ master   │      │ master  │
   │ call)   │      │          │      │         │      │ Code /  │      │          │      │         │
   │         │      │ RICE +   │      │ AC +    │      │ Cursor /│      │ runnable │      │ matrix  │
   │ AI      │      │ roadmap +│      │ scenarios│     │ Copilot)│      │ tests in │      │ + drift │
   │ summary │      │ spec     │      │ + drift │      │ writes  │      │ pytest / │      │ + coach │
   │         │      │ draft    │      │         │      │ impl    │      │ Jest / …│      │         │
   └─────────┘      └──────────┘      └─────────┘      └─────────┘      └──────────┘      └─────────┘
       ▲                  │                  │                  ▲                  ▲                  │
       │                  │                  │                  │                  │                  │
       │                  └──── spec_draft ──┘                  │                  │                  │
       │                                                        │                  │                  │
       │                                            red tests ──┘                  │                  │
       │                                                                           │                  │
       └─────────────────── decision history / chronic patterns ───────────────────┴──────────────────┘

Important. Code lives in your IDE, not in the family. Between spec and green tests, Claude Code / Cursor / Copilot writes the actual implementation. The MCP family wraps the rails — planning, spec, test, coverage, coach — and deliberately leaves the code-writing layer to whatever AI-pair-programming tool you already use. Tests generated by mk-qa-master are a runnable TODO list; the IDE loop flips them red → green.

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Install

uvx mk-plan-master    # or: pip install mk-plan-master

Add to your MCP client config:

{
  "mcpServers": {
    "mk-plan-master": {
      "command": "uvx",
      "args": ["mk-plan-master"],
      "env": {
        "PLAN_SOURCE": "markdown_local",
        "PLAN_PROJECT_ROOT": "/path/to/your/project"
      }
    }
  }
}

Works in Claude Desktop, Claude Code, Cursor, Codex CLI, Gemini CLI — any MCP client.

Claude Desktop config lives at:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json
• Linux: ~/.config/Claude/claude_desktop_config.json

Then in your AI session:

"Use mk-plan-master to score every triage idea, pick the top one, run analyze_initiative on it, then generate a spec draft and hand it to mk-spec-master."

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Tool surface (15 tools)

Grouped by role in the idea → plan → spec → memory loop.

Meta — orientation (1)

Tool	Purpose
get_plan_source_info	Active adapter + all available + version. Call first so the AI knows whether to expect markdown / Linear / JIRA / Notion semantics

Discovery — find and load ideas (2)

Tool	Purpose
list_initiatives	Enumerate initiatives from the active source (filter by status / label / limit). For Linear: triage / backlog / unstarted. For JIRA: statusCategory='To Do'. For Notion: status in (Triage / Backlog / Idea)
fetch_initiative	Pull a single initiative by id. Returns {id, source, title, body, url, status, labels, raw_metadata} — raw_metadata carries the RICE inputs (reach / impact / confidence / effort / okr)

Capture — chat / WebFetch handoff (1)

Tool	Purpose
add_initiative	Persist an idea you (the AI client) already gathered via WebFetch / chat / call notes into PLAN_PROJECT_ROOT/initiatives/<id>.md. The family does NOT crawl URLs — you summarize, this tool writes. Auto-generates IDEA-NNN if id is omitted. markdown_local only; for Linear / JIRA / Notion, create the issue in that platform

Analysis — the senior-PM SOP (1)

Tool

Purpose

analyze_initiative

Force a senior-PM analysis SOP before scoring. Returns a structured checklist (target users / competition / market signal / risks / MVP scope / out-of-scope / RICE rationale) the AI must fill in inline. Loads plan-knowledge.md context if present. Does NOT call an LLM — it scaffolds the prompt so the AI doesn't shortcut into a shallow read. Frameworks: default (7 sections), lite (4 sections), lean_canvas (9 blocks). Typical chain: add_initiative → analyze_initiative → add_initiative(overwrite=true) with the enriched body → score_initiative

Scoring — prioritize the backlog (2)

Tool	Purpose
score_initiative	Score one initiative with RICE or Impact-Effort. Pass initiative_id (RICE inputs read from raw_metadata) or raw_text + overrides for ad-hoc. Tier thresholds: P0 > 25, P1 10..25, P2 3..10, P3 < 3. Appends a scored decision to .mk-plan-master/index.json
rank_backlog	Score the whole backlog and return the top-N descending. Pure arithmetic, deterministic rationale strings. Auto-archives a snapshot to .mk-plan-master/history/<ts>.json (debounced 5 min) so get_planning_history / get_decision_signature can compute trend deltas

Bridge — the family lock-in (1)

Tool	Purpose
generate_spec_draft	The family-bridge tool. Produce a markdown spec draft shaped so mk-spec-master.parse_spec(raw_text=...) ingests it verbatim — no manual editing. Three templates: default (title / source / OKR / context / AC / out-of-scope), lite (title / context / AC), detailed (default + risks + dependencies + estimated effort). Appends a spec_generated decision to the index

Roadmap — quarterly planning (2)

Tool	Purpose
generate_roadmap	Pack the ranked backlog into a quarterly markdown roadmap, respecting an engineering capacity envelope (engineer-months × 4 person-weeks) minus a buffer (default 20%). Greedy score-per-effort packer. Output split into P0 commitments / P1 commitments / P2 stretch / Deferred / Capacity summary
analyze_roadmap_balance	Classify top-N initiatives into feature / tech_debt / strategic / unlabeled buckets, surface ratio + score-share + heuristic advisory. Label vocabularies configurable. Answers "is the roadmap balanced?" / "are we starving tech debt?"

Knowledge — methodology layer (2)

Tool	Purpose
init_plan_knowledge	Create PLAN_PROJECT_ROOT/plan-knowledge.md from a starter template — RICE / WSJF / Impact-Effort / OKR mapping / INVEST / personas + TODO sections for active OKRs / strategic bets / tech-debt zones / glossary. Idempotent
get_plan_context	Read plan-knowledge.md (with built-in fallback). Optional section filter pulls one heading. Call near the start of a planning session so the same methodology + glossary colours every score that follows

Self-reinforcement — long-running view (3)

Tool	Purpose
get_planning_history	Trend deltas (current vs ~7d / ~30d) for top-10 RICE-ranked snapshots. Surfaces churn + average score. "Are we improving?" / "Is the same idea always at the top?"
get_decision_signature	Chronic patterns: ghost initiatives (top-10 in >50% of snapshots but never spec_generated), score whiplash (RICE swings >50% between snapshots → bad data quality), orphan OKRs (in index but zero initiatives in current top-10). "Which ideas keep getting punted?"
get_telemetry	Aggregate .mk-plan-master/telemetry.jsonl (name + duration + ok only — argument values never logged). Surfaces top tools, error rates, p50 / p95 / p99 latency, dead surface (declared but never called)

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Adapter status

PLAN_SOURCE	Source	Status	Auth
markdown_local	Local initiatives/*.md with YAML-ish frontmatter	Shipped in v0.1.0	none
linear	Linear API (GraphQL), filtered to triage / backlog / unstarted state types	Shipped in v0.1.0	LINEAR_API_KEY + PLAN_PROJECT_KEY=<team-key> (optional)
jira	JIRA Cloud (REST v3, ADF → markdown), filtered to statusCategory='To Do'	Shipped in v0.1.0	JIRA_BASE_URL + JIRA_EMAIL + JIRA_API_TOKEN + PLAN_PROJECT_KEY=<project-key> (optional)
notion	Notion databases (REST v1, blocks → markdown), filtered to Status in (Triage / Backlog / Idea)	Shipped in v0.1.0	NOTION_TOKEN + PLAN_PROJECT_KEY=<database-id>

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Why analyze_initiative exists — a real case study

This is the differentiator. AI clients, by default, shortcut into a shallow read of any idea handed to them. They infer Reach / Impact / Confidence / Effort from a 2-paragraph blurb and produce a confident-looking RICE score that's mostly noise. The numbers below are from the actual dogfood corpus in mk-plan-test/ — same URL, same idea, two passes.

Pass 1 — without analyze_initiative (the AI just reads the URL and guesses):

IDEA-001  ·  一鍵式 IP 授權平台（AI + 區塊鏈）
  reach        500
  impact         2
  confidence   0.5
  effort        12  person-weeks
  out_of_scope  []  (none)
  RICE         (500 × 2 × 0.5) / 12  =  41.7   →   P0

A confident P0. Looks like a no-brainer "ship it next quarter."

Pass 2 — with analyze_initiative (the AI is forced through the senior-PM SOP first):

IDEA-002  ·  RightClick — 一鍵式 IP 授權平台（AI + 區塊鏈）
  reach        250                    ←  scoped to "active users per quarter
                                          in initial regions (Singapore + US-west
                                          social), not raw addressable market"
  impact         2                    ←  same
  confidence   0.4                    ←  dropped: logo wall is unverifiable,
                                          AI-contract legal status untested,
                                          two-sided cold-start unproven, no GMV
  effort        18  person-weeks      ←  raised: wallet 3w + AI templates 4w
                                          + contracts/NFT 3w + marketplace 3w
                                          + lawyer review + security 3w
                                          + backoffice/observability 2w
  out_of_scope  8 explicit items      ←  fiat rails, cross-chain bridging,
                                          derivative auto-royalties (v2),
                                          multi-jurisdiction custom legal,
                                          DRM, PRO-style collective rights,
                                          video/animation, enterprise SSO
  RICE         (250 × 2 × 0.4) / 18  =  11.1   →   P1

The delta — same URL, same idea, an order of magnitude more honest:

Field	Pass 1 (junior PM)	Pass 2 (senior PM SOP)	Delta
reach	500	250	scoped down
confidence	0.5	0.4	dropped — logo washing risk, AI-contract legal risk surfaced
effort	12 pw	18 pw	+6 pw for lawyer review + security
out_of_scope	0 items	8 items	explicit MVP fence
RICE	41.7	11.1	3.8× drop
tier	P0	P1	one tier down

P0 → P1 is the difference between "ship next quarter" and "validate first." analyze_initiative is the SOP that gets you there without needing a senior PM in the room. Same idea, same source URL — different rigor.

Both initiatives are in mk-plan-test/initiatives/ verbatim. Both spec drafts are in mk-plan-test/specs/. The full decision trail is in .mk-plan-master/index.json — every scored and spec_generated event with timestamps. Walkthrough with prompts + tool chains in docs/walkthrough.md.

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4 prompting workflows

Four natural-language prompts cover ~90% of real use. Each is one sentence to your AI client; the tools chain automatically.

1. Lock one idea — URL → spec_draft

"I read https://rightclickip.xyz/ — capture it as an initiative, run analyze_initiative on it, score it, and produce a detailed spec draft I can hand to mk-spec-master."

Chains: add_initiative (from your chat summary, family does NOT crawl) → analyze_initiative → add_initiative(overwrite=true) (with the enriched body) → score_initiative → generate_spec_draft(template="detailed") → mk-spec-master.parse_spec(raw_text=...).

2. Weekly backlog re-rank — trend over time

"Every Monday, rank my Linear triage backlog with RICE and show me the trend vs last week and last month."

Chains: rank_backlog(method="rice", limit=10) → get_planning_history(window_days=30). The first call auto-archives the snapshot; the second reads them all and computes deltas.

3. Senior-PM SOP on demand

"Apply the senior-PM analysis SOP to IDEA-014 — I want target users, competition, market signal, risks, MVP scope, out-of-scope, and RICE rationale before I score it."

Chains: get_plan_context (loads methodology + glossary) → fetch_initiative("IDEA-014") → analyze_initiative("IDEA-014", framework="default") → AI fills checklist in response → add_initiative(overwrite=true) → score_initiative.

4. Quarterly roadmap from Notion triage

"Pull every Notion idea in the Triage view, rank them with RICE, then pack a Q3 2026 roadmap assuming 4 engineers and 20% buffer. Tell me if the feature/tech-debt/strategic mix looks healthy."

Chains: list_initiatives(status="triage") → rank_backlog → generate_roadmap(capacity_engineer_months=12, period="Q3 2026", buffer_pct=20) → analyze_roadmap_balance.

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Self-reinforcement layer

get_planning_history + get_decision_signature + get_telemetry are the trio that makes mk-plan-master measure its own decision quality over time. The mk-spec-master v0.4 pattern, applied one step upstream.

Layer	Question it answers	Storage
History	"Are we improving? Is the same idea always at the top?"	.mk-plan-master/history/<ts>.json — auto-archived per rank_backlog call, debounced 5 min
Decision signature	"Which ideas keep getting punted (ghost)? Which scores swing wildly (whiplash)? Which OKRs have zero execution (orphan)?"	Computed from history + index.json
Telemetry	"What's the AI actually using? Which tools are slow? Which are dead surface?"	.mk-plan-master/telemetry.jsonl — append-only, name + duration + ok only, payloads never logged

Same shape as mk-spec-master's get_spec_history / get_drift_signature / get_telemetry, so if you already trust that pattern you know the layout.

The decisions[] audit trail on every initiative ("why did we deprioritize this last quarter?") is what kills the bouncing back problem. No more "didn't we discuss this in March?" — March's RICE breakdown is in the index with its confidence and effort values.

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Why this is missing from the ecosystem

Tool	Lock-in	What we do differently
Productboard	$20-50/user/mo, walled garden	MCP-native: lives where the AI lives. Read existing Linear / JIRA / Notion, don't import to a new tool
Aha!	$59-149/user/mo, enterprise	Open-source baseline, SMB / indie / AI-native segment
Linear / JIRA	Backlog UI, no triage framework, no plan→spec bridge	We add the scoring + roadmap + spec-bridge layers on top of what you already have
Cursor / Claude "ask AI to plan"	Free-form chat, no persistence	Structured outputs, JSON index, traceable decisions, snapshot history
AWS Kiro plan phase	AWS IDE only, proprietary	MCP-native, multi-client
GitHub Spec Kit	Spec-first, doesn't reach upstream into idea triage	We're the missing pre-spec layer; complementary

See docs/prd.md §4 for the full positioning.

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Status

Milestone	Scope	Status
v0.1 (4 adapters, 15 tools, RICE + Impact-Effort, generate_spec_draft, plan-knowledge, self-reinforcement)	This release	Shipped
v0.2 (Productboard adapter, cluster_feedback, WSJF method)	+1 week	Planned
v0.3 (Intercom / Zendesk adapters, compare_competitors, link_initiative_to_okr)	+2 weeks	Planned
v1.0 (production-ready, docs, integration recipes, blog series)	Q3 2026	Planned

77 tests passing on Python 3.10 / 3.11 / 3.12 / 3.13.

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Family

• mk-spec-master — AI 規格大師. Spec → scenarios → coverage matrix. generate_spec_draft output is shaped to drop into its parse_spec(raw_text=...) verbatim.
• mk-qa-master — AI 測試大師. Scenarios → runnable tests in pytest / Jest / Cypress / Go test / Maestro.

The family loop: mk-plan-master → mk-spec-master → your IDE → mk-qa-master → back into mk-spec-master coverage.

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License

MIT © 2026 Jack Kao — see LICENSE.

Plain-English version: personal use, commercial use, modification, redistribution — all allowed. The only requirement is that you keep the copyright and license notice in your copy. No warranty: if it breaks in production, you can't come after the author.

Built by Jack Kao . Part of the mk-* family: mk-qa-master + mk-spec-master + mk-plan-master.

If this saved you time, a coffee goes a long way.