What can you do with this server?

mk-spec-master is an MCP server that transforms specs from various sources into structured test scenarios, maintains a bidirectional spec ↔ test traceability matrix, and provides quality coaching — sitting between your spec source and your test runner. Spec Discovery & Loading * Connect to and fetch specs from Local Markdown, GitHub Issues, Linear, JIRA, Notion, and Figma * List, filter, and fetch individual specs; parse them to extract structured acceptance criteria (with multi-language heading support) and generate an ac_hash for drift detection Test Generation & Planning * Convert acceptance criteria into classified test scenarios (happy/edge/error) with Given/When/Then structure * Generate a complete markdown test plan in one shot, ready to hand off to mk-qa-master or other test runners Coverage & Drift Tracking * Link tests to specs manually or by scanning test directories for @spec: tags (Python, JS/TS, Go) * View a coverage matrix to instantly see which specs have no tests * Detect spec drift — specs that changed since tests were last linked — to flag potentially stale tests Spec Quality Coaching * Analyze specs for vague language, implementation-detail leakage, and unclear role references * Generate PM-facing markdown with concrete rewrite suggestions * Produce a three-layer prioritized optimization plan: coverage gaps (L1), spec quality (L2), and process drift (L3) Knowledge Management * Initialize and read a spec-knowledge.md file storing team methodology, EARS/INVEST rules, actors, and glossary Self-Reinforcement & Telemetry * Track historical optimization snapshots for trend analysis (spec count, coverage, quality, drift over time) * Identify chronic problem specs that repeatedly appear as drifted, low-quality, or unlinked * Aggregate tool-usage telemetry: call frequency, error rates, latency, and unused tools

Which integrations are available for this server?

Integration with Figma to extract specifications from file frames and comments. Integration with GitHub Issues to fetch and parse specifications and link them to test scenarios. Integration with JIRA Cloud to retrieve stories and issues as specifications. Integration with Linear API to fetch Linear tickets as specifications and maintain traceability. Integration with local Markdown files with YAML frontmatter as specification source. Integration with Notion databases to use pages as specifications.

mk-spec-master

by kao273183

Overview Schema Related Servers Score Discussions

Python

Hybrid

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.

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.

Related MCP server: Gigaspec

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.

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)

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>`

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.

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      |

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."

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.

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

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. ☕

Install Server

license - permissive license

quality

maintenance

How are these scores calculated?

Maintenance

–Maintainers

–Response time

0dRelease cycle

13Releases (12mo)

Commit activity

Resources

GitHub Repository

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