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MCP Probe Kit

by mybolide
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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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npm version npm downloads License: MIT GitHub stars

🚀 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

📚 Complete Documentation

👉

✨ 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:

Why use

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

🧭 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 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

🚀 Quick Start

Method 1: Use directly with npx (Recommended)

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:

{ "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:

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

Method 2: Global Installation

npm install -g mcp-probe-kit

Use in config file:

{ "mcpServers": { "mcp-probe-kit": { "command": "mcp-probe-kit" } } }

Restart Client

After configuration, completely quit and reopen your MCP client.

👉

💡 Usage Examples

Daily Development

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

New Feature Development

start_feature user-auth "User authentication feature" # Auto-complete: Requirements analysis → Design → Effort estimation

Bug Fixing

start_bugfix # Then paste error message # Auto-complete: Problem location → Fix solution → Test code

Product Design

start_product "Online Education Platform" --product_type=SaaS # Auto-complete: PRD → Prototype → Design system → HTML prototype

UI Development

start_ui "Login Page" --mode=auto # Auto-complete: Design system → Component generation → Code output

Project Context Documentation

# 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

Git Work Report

# 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)

👉

❓ FAQ

Q1: Tool not working or errors?

Check detailed logs:

Windows (PowerShell):

npx -y mcp-probe-kit@latest 2>&1 | Tee-Object -FilePath .\mcp-probe-kit.log

macOS/Linux:

npx -y mcp-probe-kit@latest 2>&1 | tee ./mcp-probe-kit.log

Q2: Client not recognizing tools after configuration?

  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

Q3: How to update to latest version?

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

Global installation method:

npm update -g mcp-probe-kit

👉

🤝 Contributing

Issues and Pull Requests welcome!

Improvement suggestions:

  • Add useful tools

  • Optimize existing tool prompts

  • Improve documentation and examples

  • Fix bugs

📄 License

MIT License

🔗 Related Links

Related Projects:

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