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> Talk is cheap, show me the Context.
>
> mcp-probe-kit is a protocol-level toolkit designed for developers who want AI to truly understand their project's intent. It's not just a collection of 21 tools—it's a context-aware system that helps AI agents grasp what you're building.

Languages: English | 简体中文 | 日本語 | 한국어 | Español | Français | Deutsch | Português (BR)


> 🚀 AI-Powered Complete Development Toolkit - Covering the Entire Development Lifecycle

A powerful MCP (Model Context Protocol) server providing 21 tools covering the complete workflow from product analysis to final release (Requirements → Design → Development → Quality → Release), all tools support structured output.

🎉 v3.0 Major Update: Streamlined tool count, focus on core competencies, eliminate choice paralysis, let AI do more native work

Supports All MCP Clients: Cursor, Claude Desktop, Cline, Continue, and more

Protocol Version: MCP 2025-11-25 · SDK: @modelcontextprotocol/sdk 1.25.3

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📚 Complete Documentation

👉 https://mcp-probe-kit.bytezonex.com

- Quick Start - Setup in 5 minutes
- All Tools - Complete list of 21 tools
- Best Practices - Full development workflow guide
- v3.0 Migration Guide - Upgrade from v2.x to v3.0

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✨ Core Features

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- 🔄 Workflow Orchestration (6 tools) - One-click complex development workflows
- start_feature, start_bugfix, start_onboard, start_ui, start_product, start_ralph
- 🔍 Code Analysis (3 tools) - Code quality and refactoring
- code_review, fix_bug, refactor
- 📝 Git Tools (2 tools) - Git commits and work reports
- gencommit, git_work_report
- ⚡ Code Generation (1 tool) - Test generation
- gentest
- 📦 Project Management (7 tools) - Project initialization and requirements management
- init_project, init_project_context, add_feature, estimate, interview, ask_user
- 🎨 UI/UX Tools (3 tools) - Design systems and data synchronization
- ui_design_system, ui_search, sync_ui_data

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Core and orchestration tools support structured output, returning machine-readable JSON data, improving AI parsing accuracy, supporting tool chaining and state tracking.

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All start_* orchestration tools return an execution plan in structuredContent.metadata.plan.
AI needs to call tools step by step and persist files, rather than the tool executing internally.

Plan Schema (Core Fields):
``json
{
"mode": "delegated",
"steps": [
{
"id": "spec",
"tool": "add_feature",
"args": { "feature_name": "user-auth", "description": "User authentication feature" },
"outputs": ["docs/specs/user-auth/requirements.md"]
}
]
}
`

Field Description:
- mode: Fixed as delegated
- steps: Array of execution steps
- tool: Tool name (e.g. add_feature)
- action: Manual action description when no tool (e.g. update_project_context)
- args: Tool parameters
- outputs: Expected artifacts
- when/dependsOn/note: Optional conditions and notes

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Both orchestration and atomic tools return structuredContent, common fields:
- summary: One-line summary
- status: Status (pending/success/failed/partial)
- steps: Execution steps (orchestration tools)
- artifacts: Artifact list (path + purpose)
- metadata.plan: Delegated execution plan (only start_*)
- specArtifacts: Specification artifacts (start_feature)
- estimate: Estimation results (start_feature / estimate)

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When requirements are unclear, use requirements_mode=loop in start_feature / start_bugfix / start_ui.
This mode performs 1-2 rounds of structured clarification before entering spec/fix/UI execution.

Example:
`json
{
"feature_name": "user-auth",
"description": "User authentication feature",
"requirements_mode": "loop",
"loop_max_rounds": 2,
"loop_question_budget": 5
}
`

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add_feature supports template profiles, default auto auto-selects: prefers guided when requirements are incomplete (includes detailed filling rules and checklists), selects strict when requirements are complete (more compact structure, suitable for high-capability models or archival scenarios).

Example:
`json
{
"description": "Add user authentication feature",
"template_profile": "auto"
}
`

Applicable Tools:
- start_feature passes template_profile to add_feature
- start_bugfix / start_ui also support template_profile for controlling guidance strength (auto/guided/strict)

Template Profile Strategy:
- guided: Less/incomplete requirements info, regular model priority
- strict: Requirements structured, prefer more compact guidance
- auto: Default recommendation, auto-selects guided/strict

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6 intelligent orchestration tools that automatically combine multiple basic tools for one-click complex development workflows:
- start_feature - New feature development (Requirements → Design → Estimation)
- start_bugfix - Bug fixing (Analysis → Fix → Testing)
- start_onboard - Project onboarding (Generate project context docs)
- start_ui - UI development (Design system → Components → Code)
- start_product - Product design (PRD → Prototype → Design system → HTML)
- start_ralph - Ralph Loop (Iterative development until goal completion)

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start_product is a complete product design orchestration tool, from requirements to interactive prototype:

Workflow:
1. Requirements Analysis - Generate standard PRD (product overview, feature requirements, page list)
2. Prototype Design - Generate detailed prototype docs for each page
3. Design System - Generate design specifications based on product type
4. HTML Prototype - Generate interactive prototype viewable in browser
5. Project Context - Auto-update project documentation

Structured Output Additions:
- start_product.structuredContent.artifacts: Artifact list (PRD, prototypes, design system, etc.)
- interview.structuredContent.mode: usage / questions / record

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3 UI/UX tools with start_ui as the unified entry point:
- start_ui - One-click UI development (supports intelligent mode) (orchestration tool)
- ui_design_system - Intelligent design system generation
- ui_search - UI/UX data search (BM25 algorithm)
- sync_ui_data - Sync latest UI/UX data locally

Note: start_ui automatically calls ui_design_system and ui_search, you don't need to call them separately.

Inspiration:
- ui-ux-pro-max-skill - UI/UX design system philosophy
- json-render - JSON template rendering engine

Why use sync_ui_data?

Our start_ui tool relies on a rich UI/UX database (colors, icons, charts, components, design patterns, etc.) to generate high-quality design systems and code. This data comes from npm package uipro-cli, including:
- 🎨 Color schemes (mainstream brand colors, color palettes)
- 🔣 Icon libraries (React Icons, Heroicons, etc.)
- 📊 Chart components (Recharts, Chart.js, etc.)
- 🎯 Landing page templates (SaaS, e-commerce, government, etc.)
- 📐 Design specifications (spacing, fonts, shadows, etc.)

Data Sync Strategy:
1. Embedded Data: Synced at build time, works offline
2. Cached Data: Runtime updates to ~/.mcp-probe-kit/ui-ux-data/
3. Manual Sync: Use sync_ui_data to force update latest data

This ensures start_ui can generate professional-grade UI code even offline.

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2 interview tools to clarify requirements before development:
- interview - Structured requirements interview
- ask_user - AI proactive questioning

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🧭 Tool Selection Guide

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Use orchestration tools (start_*) when:
- ✅ Need complete workflow (multiple steps)
- ✅ Want to automate multiple tasks
- ✅ Need to generate multiple artifacts (docs, code, tests, etc.)

Use individual tools when:
- ✅ Only need specific functionality
- ✅ Already have project context docs
- ✅ Need more fine-grained control

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| Scenario | Recommended Tool | Reason |
|---------|-----------------|--------|
| Develop new feature (complete flow) | start_feature | Auto-complete: spec→estimation |
| Only need feature spec docs | add_feature | More lightweight, only generates docs |
| Fix bug (complete flow) | start_bugfix | Auto-complete: analysis→fix→test |
| Only need bug analysis | fix_bug | Faster, only analyzes problem |
| Generate design system | ui_design_system | Directly generate design specs |
| Develop UI components | start_ui | Complete flow: design→components→code |
| Product design (requirements to prototype) | start_product | One-click: PRD→prototype→HTML |
| One-sentence requirement analysis | init_project | Generate complete project spec docs |
| Project onboarding docs | init_project_context | Generate tech stack/architecture/conventions |

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🚀 Quick Start

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No installation needed, use the latest version directly.

#### Cursor / Cline Configuration

Config file location:
- Windows: %APPDATA%\Cursor\User\globalStorage\saoudrizwan.claude-dev\settings\cline_mcp_settings.json
- macOS: ~/Library/Application Support/Cursor/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json
- Linux: ~/.config/Cursor/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json

Config content:
`json
{
"mcpServers": {
"mcp-probe-kit": {
"command": "npx",
"args": ["mcp-probe-kit@latest"]
}
}
}
`

#### Claude Desktop Configuration

Config file location:
- Windows: %APPDATA%\Claude\claude_desktop_config.json
- macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
- Linux: ~/.config/Claude/claude_desktop_config.json

Config content:
`json
{
"mcpServers": {
"mcp-probe-kit": {
"command": "npx",
"args": ["-y", "mcp-probe-kit@latest"]
}
}
}
`

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`bash
npm install -g mcp-probe-kit
`

Use in config file:
`json
{
"mcpServers": {
"mcp-probe-kit": {
"command": "mcp-probe-kit"
}
}
}
`

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After configuration, completely quit and reopen your MCP client.

👉 Detailed Installation Guide

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💡 Usage Examples

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`bash
code_review @feature.ts # Code review
gentest @feature.ts # Generate tests
gencommit # Generate commit message
`

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`bash
start_feature user-auth "User authentication feature"

Auto-complete: Requirements analysis → Design → Effort estimation

`

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`bash
start_bugfix

Then paste error message

Auto-complete: Problem location → Fix solution → Test code

`

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`bash
start_product "Online Education Platform" --product_type=SaaS

Auto-complete: PRD → Prototype → Design system → HTML prototype

`

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`bash
start_ui "Login Page" --mode=auto

Auto-complete: Design system → Component generation → Code output

`

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`bash

Single file mode (default) - Generate a complete project-context.md

init_project_context

Modular mode - Generate 6 category docs (suitable for large projects)

init_project_context --mode=modular

Generates: project-context.md (index) + 5 category docs

`

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`bash

Generate daily report

git_work_report --date 2026-02-03

Generate weekly report

git_work_report --start_date 2026-02-01 --end_date 2026-02-07

Save to file

git_work_report --date 2026-02-03 --output_file daily-report.md

Auto-analyze Git diff, generate concise professional report

If direct command fails, auto-provides temp script solution (auto-deletes after execution)

`

👉 More Usage Examples

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❓ FAQ

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Check detailed logs:

Windows (PowerShell):
`powershell
npx -y mcp-probe-kit@latest 2>&1 | Tee-Object -FilePath .\mcp-probe-kit.log
`

macOS/Linux:
`bash
npx -y mcp-probe-kit@latest 2>&1 | tee ./mcp-probe-kit.log
`

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1. Restart client (completely quit then reopen)
2. Check config file path is correct
3. Confirm JSON format is correct, no syntax errors
4. Check client developer tools or logs for error messages

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npx method (Recommended):
Use @latest tag in config, automatically uses latest version.

Global installation method:
`bash
npm update -g mcp-probe-kit
``

👉 More FAQ

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🤝 Contributing

Issues and Pull Requests welcome!

Improvement suggestions:
- Add useful tools
- Optimize existing tool prompts
- Improve documentation and examples
- Fix bugs

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📄 License

MIT License

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🔗 Related Links

- Author: Kyle (小墨)
- GitHub: mcp-probe-kit
- npm: mcp-probe-kit
- Documentation: https://mcp-probe-kit.bytezonex.com

Related Projects:
- Model Context Protocol (MCP) - Official MCP protocol docs
- GitHub Spec-Kit - GitHub spec-driven development toolkit
- ui-ux-pro-max-skill - UI/UX design system philosophy source
- json-render - JSON template rendering engine inspiration
- uipro-cli - UI/UX data source

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