What can you do with this server?

mcp-toolz is an MCP server for Claude Code that provides persistent context and todo management with multi-AI feedback capabilities. Context Management: Save, search, list, get, and delete development contexts (conversations, code, suggestions, errors) with titles, content, tags, and automatic session/project tracking. Todo Management: Save, restore, search, list, get, and delete todo snapshots with items containing content, status (pending/in_progress/completed), and activeForm metadata. AI Feedback: Query ChatGPT, Claude, Gemini, and DeepSeek for analysis or second opinions on saved contexts with custom questions. Session Continuity: Automatically tracks session IDs, timestamps, and project paths to restore work state across Claude Code restarts. Project Organization: Filter and organize contexts and todos by project directory with full-text search capabilities. Cross-Session Sharing: Shared SQLite database enables data synchronization across multiple sessions and machines (e.g., via cloud storage). Passive Discovery: Provides MCP resources (mcp-toolz://contexts/..., mcp-toolz://todos/...) for automatic display of recent project contexts and active todo lists without explicit tool calls.

Which integrations are available for this server?

Allows querying Gemini AI models for analysis and feedback on saved contexts, with support for custom questions about code, architecture decisions, and debugging. Allows querying ChatGPT models for analysis and feedback on saved contexts, with support for custom questions about code, architecture decisions, and debugging.

How do I use mcp-toolz?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@mcp-toolz save this context about implementing user authentication" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

MCP Toolz

mcp-name: io.github.taylorleese/mcp-toolz

GitHub issues GitHub last commit codecov PyPI version Python MCP License: MIT

pre-commit OpenSSF Best Practices OpenSSF Scorecard Dependabot

MCP server for Claude Code that provides multi-LLM feedback tools and clipboard image capture.

Features

Multi-LLM Feedback: Get second opinions from ChatGPT (OpenAI), Claude (Anthropic), Gemini (Google), and DeepSeek
Clipboard Image Capture: Paste images from your macOS clipboard directly into Claude Code for analysis
MCP Integration: Works with Claude Code via the Model Context Protocol

Related MCP server: Todoist MCP

Quick Start

Installation

From PyPI (Recommended)

pip install mcp-toolz

From Source (Development)

# Clone the repository
git clone https://github.com/taylorleese/mcp-toolz.git
cd mcp-toolz

# Create and activate virtual environment
python3 -m venv venv
source venv/bin/activate  # macOS/Linux
# or: venv\Scripts\activate  # Windows

# Install in editable mode with dev dependencies
pip install -e ".[dev]"

Configuration

# Set your API keys as environment variables (at least one required for AI feedback tools)
export OPENAI_API_KEY=sk-...           # For ChatGPT
export ANTHROPIC_API_KEY=sk-ant-...    # For Claude
export GOOGLE_API_KEY=...              # For Gemini
export DEEPSEEK_API_KEY=sk-...         # For DeepSeek

# Or create a .env file (if installing from source)
cp .env.example .env
# Edit .env and add your API keys

MCP Server Setup

Add to your Claude Code MCP settings:

If installed via pip:

{
  "mcpServers": {
    "mcp-toolz": {
      "command": "python",
      "args": ["-m", "mcp_server"],
      "env": {
        "OPENAI_API_KEY": "sk-...",
        "ANTHROPIC_API_KEY": "sk-ant-...",
        "GOOGLE_API_KEY": "...",
        "DEEPSEEK_API_KEY": "sk-..."
      }
    }
  }
}

If installed from source:

{
  "mcpServers": {
    "mcp-toolz": {
      "command": "python",
      "args": ["-m", "mcp_server"],
      "cwd": "/absolute/path/to/mcp-toolz",
      "env": {
        "PYTHONPATH": "/absolute/path/to/mcp-toolz/src"
      }
    }
  }
}

Restart Claude Code to load the MCP server.

MCP Server Tools

AI Feedback Tools

Get second opinions from multiple LLMs on code, architecture decisions, and implementation plans:

ask_chatgpt - Get ChatGPT's analysis (supports custom questions)
ask_claude - Get Claude's analysis (supports custom questions)
ask_gemini - Get Gemini's analysis (supports custom questions)
ask_deepseek - Get DeepSeek's analysis (supports custom questions)

Clipboard Image Tool

paste_image - Capture an image from the macOS clipboard for analysis (supports optional question)

Usage Examples

Get Multiple AI Perspectives

I'm deciding between Redis and Memcached for caching user sessions.
Ask ChatGPT for their analysis.

Follow up with:

"Ask Claude the same question for comparison"
"Ask Gemini for another perspective"
"What does DeepSeek think about this?"

Analyze a Clipboard Image

Copy an image to your clipboard (screenshot, diagram, error message, etc.), then:

Analyze my clipboard image

Or with a specific question:

What's wrong with the UI layout in my clipboard image?

Debug with Multiple Perspectives

I'm getting "TypeError: Cannot read property 'map' of undefined" in my React component.
The error occurs in UserList.jsx when rendering the users array.
Ask ChatGPT and Claude for debugging suggestions.

Environment Variables

# Required (at least one for AI feedback tools)
OPENAI_API_KEY=sk-...                              # Your OpenAI API key
ANTHROPIC_API_KEY=sk-ant-...                       # Your Anthropic API key
GOOGLE_API_KEY=...                                 # Your Google API key (for Gemini)
DEEPSEEK_API_KEY=sk-...                            # Your DeepSeek API key

# Optional
MCP_TOOLZ_MODEL=gpt-5                                         # OpenAI model (default: gpt-5)
MCP_TOOLZ_CLAUDE_MODEL=claude-sonnet-4-5-20250929             # Claude model
MCP_TOOLZ_GEMINI_MODEL=gemini-2.0-flash-thinking-exp-01-21   # Gemini model
MCP_TOOLZ_DEEPSEEK_MODEL=deepseek-chat                        # DeepSeek model

Troubleshooting

"Error 401: Invalid API key"

Verify API keys are set in .env or environment variables
Check billing is enabled on your API provider account

"No module named context_manager"

Use PYTHONPATH=src before running Python directly
Or install via pip: pip install mcp-toolz

"No image found in clipboard"

Copy an image first (screenshot, right-click > Copy Image, etc.)
The paste_image tool requires macOS (uses AppleScript to read the clipboard)

Project Structure

mcp-toolz/
├── src/
│   ├── mcp_server/              # MCP server for Claude Code
│   │   └── server.py            # MCP tools and handlers
│   └── context_manager/         # Client implementations
│       ├── openai_client.py     # ChatGPT API client
│       ├── anthropic_client.py  # Claude API client
│       ├── gemini_client.py     # Gemini API client
│       ├── deepseek_client.py   # DeepSeek API client
│       └── clipboard.py         # macOS clipboard image capture
├── tests/                       # pytest tests
├── requirements.in
└── requirements.txt

Development

Setup for Contributors

# Clone and install
git clone https://github.com/taylorleese/mcp-toolz.git
cd mcp-toolz
python3 -m venv venv
source venv/bin/activate
pip install -r requirements-dev.txt

# Install pre-commit hooks (IMPORTANT!)
pre-commit install

# Copy and configure .env
cp .env.example .env
# Edit .env with your API keys

Running Tests

source venv/bin/activate
pytest

Code Quality

pre-commit Code style: black Ruff mypy isort security: bandit

# Run all checks (runs automatically on commit after pre-commit install)
pre-commit run --all-files

# Individual tools
black .
ruff check .
mypy src/

License

MIT

mcp-toolz