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Context management, todo persistence, and multi-AI perspectives for Claude Code
Context management, todo persistence, and multi-AI perspectives for Claude Code
Valid MCP server (1 strong, 1 medium validity signals). No known CVEs in dependencies. Package registry verified. Imported from the Official MCP Registry.
3 files analyzed · 1 issue found
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Set these up before or after installing:
Environment variable: OPENAI_API_KEY
Environment variable: ANTHROPIC_API_KEY
Environment variable: GOOGLE_API_KEY
Environment variable: DEEPSEEK_API_KEY
Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-taylorleese-mcp-toolz": {
"env": {
"GOOGLE_API_KEY": "your-google-api-key-here",
"OPENAI_API_KEY": "your-openai-api-key-here",
"DEEPSEEK_API_KEY": "your-deepseek-api-key-here",
"ANTHROPIC_API_KEY": "your-anthropic-api-key-here"
},
"args": [
"mcp-toolz"
],
"command": "uvx"
}
}
}From the project's GitHub README.
mcp-name: io.github.taylorleese/mcp-toolz
MCP server for Claude Code that provides multi-LLM feedback tools.
pip install mcp-toolz
# 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]"
# 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
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.
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)/resolve-github-alertsAutomatically triages and resolves GitHub security alerts (Dependabot, code scanning, secret scanning). Run it in Claude Code to:
/resolve-github-alerts
I'm deciding between Redis and Memcached for caching user sessions.
Ask ChatGPT for their analysis.
Follow up with:
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.
# 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
.env or environment variablesPYTHONPATH=src before running Python directlypip install mcp-toolzmcp-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
├── tests/ # pytest tests
├── requirements.in
└── requirements.txt
# 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
source venv/bin/activate
pytest
# Run all checks (runs automatically on commit after pre-commit install)
pre-commit run --all-files
# Individual tools
black .
ruff check .
mypy src/
MIT
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