Probe Kit

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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.

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🚀 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

📦 21 Tools

• 🔄 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

🎯 Structured Output

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

🧭 Delegated Orchestration Protocol

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):

{
  "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

🧩 Structured Output Field Specification (Key Fields)

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)

🧠 Requirements Clarification Mode (Requirements Loop)

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:

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

🧩 Template System (Regular Model Friendly)

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:

{
  "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

🔄 Workflow Orchestration

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)

🚀 Product Design Workflow

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

🎨 UI/UX Pro Max

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.

🎤 Requirements Interview

2 interview tools to clarify requirements before development:

• interview - Structured requirements interview
• ask_user - AI proactive questioning

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🧭 Tool Selection Guide

When to use orchestration tools vs individual tools?

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

Common Scenario Selection

Scenario	Recommended Tool	Reason
Develop new feature (complete flow)	start_feature	Auto-complete: spec→estimation
Only need feature spec do

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README truncated. View full README on GitHub.