Manage AI Contexts and Tasks Across Sessions

MCP Toolz

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

MCP server for Claude Code that provides multi-LLM feedback tools.

Features

• Multi-LLM Feedback: Get second opinions from ChatGPT (OpenAI), Gemini (Google), and DeepSeek
• MCP Integration: Works with Claude Code via the Model Context Protocol

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 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": "mcp-toolz",
      "args": [],
      "env": {
        "OPENAI_API_KEY": "sk-...",
        "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_gemini - Get Gemini's analysis (supports custom questions)
• ask_deepseek - Get DeepSeek's analysis (supports custom questions)

Claude Code plugins

This repo doubles as a Claude Code plugin marketplace. Install all four with:

/plugin marketplace add taylorleese/mcp-toolz
/plugin install mcp-toolz-server@mcp-toolz
/plugin install precommit-detect@mcp-toolz
/plugin install revise-all-docs@mcp-toolz
/plugin install resolve-github-alerts@mcp-toolz

mcp-toolz-server

Installs the mcp-toolz MCP server in Claude Code without manual editing of ~/.claude.json. Once installed, the three tools (ask_chatgpt, ask_gemini,
ask_deepseek) are available to the model in any Claude Code session. The plugin runs the server via uvx --from mcp-toolz python -m mcp_server, so PyPI
is still the underlying distribution channel - this is purely an installation-ergonomics layer for Claude Code users.

Required env vars (set in your shell or via direnv/.envrc): OPENAI_API_KEY, GOOGLE_API_KEY, DEEPSEEK_API_KEY. Each is independently optional - the
corresponding tool just returns an error if its key is unset.

For Cursor / Zed / Claude Desktop users: keep configuring the MCP server manually via your client's standard mechanism. Claude Code plugins don't propagate
to other clients.

precommit-detect

Read-only check for pre-commit setup state. Registers SessionStart and PostToolUse:EnterWorktree hooks that detect whether the current repo's
.pre-commit-config.yaml is wired up - pre-commit binary present, .git/hooks/pre-commit installed, Docker daemon reachable when the config requires it.
When something is missing, the hook surfaces the gap as additionalContext so Claude can walk you through approval-gated installs (one prompt per missing
item - never auto-installs).

revise-all-docs

Two ways to keep CLAUDE.md, README.md, and docs/**/*.md in sync - pick by intent.

/revise-all-docs - "I just finished some work. Capture what we learned."

Reads the current conversation, pulls out commands discovered, gotchas hit, and patterns enforced, and proposes additions to the right doc file
for each finding (project-internal context → CLAUDE.md, user-facing onboarding → README.md, deeper how-to → docs/). Run this at the end of
a session that uncovered something worth recording.

/improve-all-docs - "Forget the session. Audit the docs as they stand today."

Statically scans every doc file, scores each against type-appropriate rubrics (install steps actually work? public command/API surface
complete? versions and paths current? intra-doc links resolve? duplicated content?), then proposes targeted fixes - including deletions of
stale or duplicated content, not just additions. Run this during cleanup passes, before a release, or when docs feel out of sync with the code.

The all-docs-improver skill is the same audit auto-invoked when you ask in plain language ("are my docs up to date?", "check the README and
docs"). The slash command is explicit; the skill is hands-free.

Required dependency

Both surfaces delegate CLAUDE.md work to the official claude-md-management plugin:

/plugin install claude-md-management@anthropics

resolve-github-alerts

Triages and resolves GitHub security alerts (Dependabot, code scanning, secret scanning) across pip / pip-tools / poetry / uv / npm / yarn / pnpm / cargo / go-modules / Docker / GitHub Actions ecosystems. Run it in any repo to:

• Fix failing Dependabot PRs (lint/test issues)
• Bump vulnerable dependencies and recompile lockfiles
• Remediate code scanning and secret scanning alerts
• Submit a single PR with all fixes for manual review

Auto-detects the project's verify commands (Makefile targets, pre-commit, ruff, pytest, npm scripts) - no per-project configuration required.

/resolve-github-alerts

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 Gemini for another perspective"
• "What does DeepSeek think about this?"

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 Gemini for debugging suggestions.

Environment Variables

# Required (at least one for AI feedback tools)
OPENAI_API_KEY=sk-...                              # Your OpenAI 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_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

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
│       ├── gemini_client.py     # Gemini API client
│       └── deepseek_client.py   # DeepSeek API client
├── 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

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

# Individual tools
black .
ruff check .
mypy src/