MK Spec Master

AI 規格大師 — specs in, scenarios out. Bidirectional traceability so you always know what's tested.

English · 繁體中文

Spec-driven testing over MCP. Turn Linear / JIRA / GitHub Issues / Notion / Figma / Markdown specs into runnable scenarios, hand off to any test runner via mk-qa-master, and keep a live spec ↔ test coverage matrix.

🟢 Alpha — v0.4: self-reinforcement. 18 tools + 6 adapters. Snapshots archived per get_optimization_plan call → trend analysis + chronic-spec detection + tool-usage telemetry. Full design in docs/prd.md.

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What this is

An MCP server that turns specs — Linear tickets, JIRA stories, GitHub Issues, Notion pages, Figma annotations, plain Markdown — into structured test scenarios, hands them to any test runner (via mk-qa-master or directly), and maintains a live spec ↔ test coverage matrix.

Sibling to mk-qa-master in the mk-* family of opinionated AI-QA MCPs.

What this is NOT

It's not	Use this instead
A spec editor	Linear / JIRA / Notion / Markdown — keep writing specs where you already do
A test runner	mk-qa-master (pytest / Jest / Cypress / Go test / Maestro)
An issue tracker UI	Linear / JIRA / Notion's native interface
A spec → code generator	GitHub Spec Kit, AWS Kiro
An LLM	Leverages your AI client (Claude / Cursor / Codex / Gemini) for the reasoning

mk-spec-master sits between your spec source and your test runner — purely about the spec ↔ test link, the coverage matrix that lives on top, and the quality coach that grades both.

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Tool surface (15 tools)

Grouped by role. Each group is a layer in the spec→test→coverage→coach loop.

Meta — orientation (1)

Tool	Purpose
get_spec_source_info	Active adapter + all available. Call first so the AI knows whether to expect Linear / JIRA / Notion / Figma / Markdown semantics

Discovery — find and load specs (3)

Tool	Purpose
list_specs	Enumerate specs from the active source (filter by status / label / limit)
fetch_spec	Pull a single spec's full content by id
parse_spec	Heuristic AC extraction (en + zh-TW + zh-CN headings supported); accepts spec_id or raw_text. Returns _meta.ac_hash for drift detection

Generation — specs → testable artifacts (2)

Tool	Purpose
extract_scenarios	AC → scenarios with happy / edge / error classification (negation-aware) and best-effort Given/When/Then split
generate_test_plan	One-shot fetch + parse + extract → markdown plan ready to feed to mk-qa-master.generate_test(business_context=...)

Coverage & drift — the traceability layer (4)

Tool	Purpose
link_test_to_spec	Record that a test verifies a spec (writes to SPEC_PROJECT_ROOT/.mk-spec-master/index.json). Stores title / source / url / ac_hash for the matrix and drift report
auto_link_tests	Scan a test directory for @spec: <ID> tags and link them automatically. Python / JS / TS / Go supported. dry_run previews without writing
get_coverage_matrix	Spec × test grid — answer "which specs have no tests" in one call
get_drift_report	Re-fetch each linked spec, recompute ac_hash, compare. Buckets into fresh / drifted / unknown / stranded

Coach — quality + prioritization (3)

Tool	Purpose
analyze_spec_quality	Heuristic findings on vague language, implementation-leak AC, unclear role refs (the differentiator vs Kiro / Spec Kit)
propose_spec_improvements	Take analyze output → PM-facing markdown with concrete rewrites
get_optimization_plan	Three-layer prioritized plan: coverage gaps (L1) + spec-quality (L2) + process drift (L3). The "what should we fix next" tool

Knowledge — domain methodology (2)

Tool	Purpose
init_spec_knowledge	Create SPEC_PROJECT_ROOT/spec-knowledge.md from a starter template (EARS, INVEST, AC quality rules + TODO sections for your team's rules / actors / glossary). Idempotent
get_spec_context	Read the spec-knowledge file (with built-in fallback). Optional section filter pulls one heading at a time. Call near the start of every session

Self-reinforcement — long-running view (3, v0.4)

Tool	Purpose
get_spec_history	Last N snapshots archived by get_optimization_plan, with trend deltas (current vs ~7d, vs ~30d) for spec / coverage / quality / drift counters. "Are we improving?"
get_drift_signature	Scan recent snapshots for specs that repeatedly land in drifted / unknown / low-quality buckets — chronic patterns. "Which specs keep causing trouble?"
get_telemetry	Aggregate the tool-usage log: which tools get called most, error rates, p50 / p95 latency, dead-surface (declared but never called)

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Adapter status

SPEC_SOURCE	Source	Status	Auth
markdown_local	Local *.md with YAML-ish frontmatter	✅ since 0.1.0	none
github_issues	GitHub Issues via gh CLI	✅ since 0.1.0	gh auth login or GITHUB_TOKEN
linear	Linear API (GraphQL)	✅ since 0.2.2	LINEAR_API_KEY + SPEC_PROJECT_KEY=<team-key> (optional)
jira	JIRA Cloud (REST v3, ADF → markdown)	✅ since 0.2.3	JIRA_BASE_URL + JIRA_EMAIL + JIRA_API_TOKEN + SPEC_PROJECT_KEY=<project-key> (optional)
notion	Notion databases (REST v1, blocks → markdown)	✅ since 0.3.0	NOTION_TOKEN + SPEC_PROJECT_KEY=<database-id>
figma	Figma file frames (TEXT nodes + comments → markdown)	✅ since 0.3.1	FIGMA_TOKEN + SPEC_PROJECT_KEY=<file-key>

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Common workflows

Four patterns cover ~90% of real use. Each is one sentence to the AI client; the tools chain automatically.

1. Spec → test → run → coverage (the main loop)

"Fetch LIN-123 from Linear, extract scenarios, generate Playwright tests with mk-qa-master, run them, and update the coverage matrix."

Chains: fetch_spec → parse_spec → extract_scenarios → mk-qa-master.generate_test (×N) → link_test_to_spec (×N) → mk-qa-master.run_tests → get_coverage_matrix.

2. Spec health check

"Review every in-progress spec for quality issues and give me a prioritized improvement plan."

Chains: list_specs(status="in-progress") → analyze_spec_quality → propose_spec_improvements → get_optimization_plan.

3. Rebuild traceability after a refactor

"Sync the spec ↔ test index from the test source — I just renamed a bunch of files."

Chains: auto_link_tests → get_coverage_matrix. Tests need @spec: <ID> docstring tags for auto-link to work; comment-above-function and docstring-inside both supported.

4. Session warmup

"Before we work on specs today: load the spec-knowledge methodology and tell me which source is active."

Chains: get_spec_source_info → get_spec_context. Cheap, sets the methodology + adapter context for everything that follows.

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Sample output

get_optimization_plan markdown (excerpt)

# Optimization plan

_Coverage matrix: 23 spec(s) tracked, 4 untested._
_Spec quality: 23 spec(s) analyzed, 17 finding(s)._
_Drift: 2 drifted, 0 stranded, 5 without ac_hash._

## 🔴 Layer 1 — Coverage gaps

**Specs with zero tests** (ranked first — every business risk lives here):
- `LIN-204` — Apply promo code at checkout
- `LIN-211` — Refund flow

## 🟡 Layer 2 — Spec quality

### `LIN-098` — Checkout latency  (score: 80/100, findings: 4)
- 🟡 `ac-1`: Quantify (e.g., 'response within 200 ms')  (evidence: `fast`)
- 🔴 `ac-3`: Rewrite to describe what the user observes  (evidence: `redis`)

## 🔵 Layer 3 — Process drift

**Drifted** (spec changed since link — review affected tests):
- `LIN-123` — Apply discount at checkout · 4 test(s) potentially stale

get_coverage_matrix markdown (excerpt)

# Coverage matrix

- Specs tracked: 23
- Specs shown (min_tests=0): 23
- Specs with zero tests: 4

| Spec    | Title                          | Tests | Last status |
|---------|--------------------------------|------:|-------------|
| `LIN-204` | Apply promo code at checkout |     0 | —           |
| `LIN-123` | Apply discount at checkout   |     4 | passed      |

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Install

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

Add to your MCP client config:

{
  "mcpServers": {
    "mk-spec-master": {
      "command": "uvx",
      "args": ["mk-spec-master"],
      "env": {
        "SPEC_SOURCE": "markdown_local",
        "SPEC_PROJECT_ROOT": "/path/to/your/project"
      }
    }
  }
}

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 Claude / Cursor / Codex / Gemini CLI:

"Use mk-spec-master to parse SPEC-001, extract scenarios, and hand them to mk-qa-master so we can generate Playwright tests."

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Why this is missing from the ecosystem

Tool	Lock-in	What we do differently
AWS Kiro	AWS IDE only, proprietary	MCP-native, multi-client, open source
Jama Connect MCP	$50k+/year, enterprise-only	SMB / indie / AI-native segment
GitHub Spec Kit	spec→code; runtime test coverage out of scope	We add runtime test coverage
testomat.io / JIRA MCPs	Single source (JIRA), SaaS lock	Multi-source, file-based index, no lock

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

Walkthrough — spec → test → coverage (long form)

Given a Linear ticket LIN-123 "Apply discount at checkout" with 4 acceptance criteria:

You: Use mk-spec-master to fetch LIN-123, extract scenarios, generate
     Playwright tests with mk-qa-master, run them, and report coverage.

The AI client chains:

mk-spec-master.fetch_spec("LIN-123")
mk-spec-master.parse_spec(spec_id="LIN-123")        → 4 AC + ac_hash
mk-spec-master.extract_scenarios(...)                → 1 happy + 3 error
mk-spec-master.generate_test_plan(spec_id="LIN-123")

for scenario in plan:
  mk-qa-master.generate_test(business_context=scenario.gherkin)
  mk-spec-master.link_test_to_spec(spec_id="LIN-123", test_node_id=..., ac_hash=...)

mk-qa-master.run_tests
mk-spec-master.get_coverage_matrix

The traceability index now records all 4 links with their AC hashes. Next sprint, when the spec changes, get_drift_report flags every test whose linked spec has moved — re-run the chain only for those.

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Status

Milestone	Target	Status
v0.1 (MVP — markdown_local + github_issues, 7 tools)	June 2026	✅ Shipped
v0.2 (Linear, JIRA, coverage matrix, spec-quality coach, drift report)	Aug 2026	✅ Complete (0.2.3)
v0.3 (Notion, Figma, auto-link, optimization plan)	Oct 2026	✅ Complete (0.3.3)
v1.0 (production-ready, docs, integration recipes)	Q4 2026	⬜

Family

• mk-qa-master — AI 測試大師, the test-runner sibling. Tests run via mk-qa-master; coverage tracked here.
• More mk-* MCPs in design (mk-perf-master, mk-a11y-master).

License

MIT © 2026 Jack Kao — see LICENSE (中文翻譯參考：LICENSE.zh-TW.md; the English version is authoritative).

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 something in production, you can't come after the author.

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