Skip to main content
Glama

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.


Why this exists

The AI-driven dev pipeline today looks like this:

???  →  Spec  →  Code  →  Test  →  Coverage  →  Coach
(gap)   mk-spec  IDE     mk-qa    mk-spec      both

The mk-* family already shipped two MCPs covering the right half:

  • mk-spec-master — specs in, scenarios out, coverage matrix

  • mk-qa-master — scenarios in, runnable tests out (pytest / Jest / Cypress / Go test / Maestro)

mk-plan-master closes the upstream gap. The piece nobody has built MCP-native yet: turn a pile of 30–200 ideas (chat snippets, customer calls, URLs, gut hunches) into a prioritized, RICE-scored quarterly roadmap, and emit a spec draft that drops straight into mk-spec-master.parse_spec(raw_text=...) — no manual reformatting, no copy-paste fragility.

It's the planning MCP that also 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.


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.


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.

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."


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_initiativeanalyze_initiativeadd_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)


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>


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.


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_initiativeadd_initiative(overwrite=true) (with the enriched body) → score_initiativegenerate_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_backloggenerate_roadmap(capacity_engineer_months=12, period="Q3 2026", buffer_pct=20)analyze_roadmap_balance.


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.


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.


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.


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.


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.

Install Server
A
license - permissive license
A
quality
B
maintenance

Maintenance

Maintainers
Response time
Release cycle
1Releases (12mo)

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/kao273183/mk-plan-master'

If you have feedback or need assistance with the MCP directory API, please join our Discord server