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    Mcp Toolz

    MCP Tools that may or may not be useful to others.

    2 stars
    Python
    Updated Nov 3, 2025

    Table of Contents

    • Features
    • Quick Start
    • Installation
    • From PyPI (Recommended)
    • From Source (Development)
    • Configuration
    • MCP Server Setup
    • MCP Server Tools
    • AI Feedback Tools
    • Claude Code plugins
    • mcp-toolz-server
    • precommit-detect
    • revise-all-docs
    • /revise-all-docs — "I just finished some work. Capture what we learned."
    • /improve-all-docs — "Forget the session. Audit the docs as they stand today."
    • Required dependency
    • resolve-github-alerts
    • Usage Examples
    • Get Multiple AI Perspectives
    • Debug with Multiple Perspectives
    • Environment Variables
    • Troubleshooting
    • "Error 401: Invalid API key"
    • "No module named context_manager"
    • Project Structure
    • Development
    • Setup for Contributors
    • Running Tests
    • Code Quality
    • License

    Table of Contents

    • Features
    • Quick Start
    • Installation
    • From PyPI (Recommended)
    • From Source (Development)
    • Configuration
    • MCP Server Setup
    • MCP Server Tools
    • AI Feedback Tools
    • Claude Code plugins
    • mcp-toolz-server
    • precommit-detect
    • revise-all-docs
    • /revise-all-docs — "I just finished some work. Capture what we learned."
    • /improve-all-docs — "Forget the session. Audit the docs as they stand today."
    • Required dependency
    • resolve-github-alerts
    • Usage Examples
    • Get Multiple AI Perspectives
    • Debug with Multiple Perspectives
    • Environment Variables
    • Troubleshooting
    • "Error 401: Invalid API key"
    • "No module named context_manager"
    • Project Structure
    • Development
    • Setup for Contributors
    • Running Tests
    • Code Quality
    • License

    Documentation

    MCP Toolz

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

    CI

    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.

    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)

    bash
    pip install mcp-toolz

    From Source (Development)

    bash
    # 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

    bash
    # 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:

    json
    {
      "mcpServers": {
        "mcp-toolz": {
          "command": "mcp-toolz",
          "args": [],
          "env": {
            "OPENAI_API_KEY": "sk-...",
            "GOOGLE_API_KEY": "...",
            "DEEPSEEK_API_KEY": "sk-..."
          }
        }
      }
    }

    If installed from source:

    json
    {
      "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:

    text
    /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:

    text
    /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.

    text
    /resolve-github-alerts

    Usage Examples

    Get Multiple AI Perspectives

    text
    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

    text
    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

    bash
    # 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

    text
    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

    bash
    # 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

    bash
    source venv/bin/activate
    pytest

    Code Quality

    pre-commit

    Code style: black

    Ruff

    mypy

    isort

    security: bandit

    bash
    # 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

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