torchtalk-setup

name	torchtalk-setup
description	Install and configure TorchTalk MCP server for PyTorch cross-language analysis

Name

odh-ai-helpers:torchtalk-setup

Synopsis

/torchtalk:setup

Description

The torchtalk:setup command guides you through the complete installation and configuration of the TorchTalk MCP server, which enables cross-language binding analysis for PyTorch codebases (Python/C++/CUDA). This command:

• Checks for and installs required dependencies (Python 3.10+, pip)
• Clones and builds TorchTalk from source
• Locates or clones PyTorch source code
• Configures TorchTalk with the PyTorch source path
• Registers the MCP server with Claude Code at user scope
• Verifies the complete setup

After setup, you can use /torchtalk:trace and the full suite of TorchTalk MCP tools across all Claude Code sessions.

Implementation

Phase 1: Check Prerequisites

1.1 Verify Python Version

python3 --version

Minimum required: Python 3.10

If Python is too old or missing:

Python 3.10+ is required for TorchTalk.

Install or upgrade Python:
- macOS: brew install python@3.12
- Ubuntu/Debian: sudo apt install python3.12
- Fedora/RHEL: sudo dnf install python3.12

1.2 Verify pip

python3 -m pip --version

If pip is missing, provide platform-specific installation instructions.

1.3 Check for Existing TorchTalk Installation

which torchtalk

If torchtalk is found:

• Run torchtalk status to show current configuration
• Ask the user if they want to:
  1. Reconfigure the existing installation
  2. Reinstall from scratch
  3. Exit setup

Phase 2: Install TorchTalk

2.1 Determine Installation Location

Ask the user where they want to install TorchTalk:

Where would you like to install TorchTalk?

1. ~/src/torchtalk (recommended)
2. ~/.local/share/torchtalk
3. Custom path

Store the chosen directory as $TORCHTALK_DIR.

2.2 Clone the Repository

mkdir -p "$(dirname "$TORCHTALK_DIR")"
git clone https://github.com/adabeyta/torchtalk.git "$TORCHTALK_DIR"

Handle errors:

• Directory already exists: Ask to use existing or remove and re-clone
• Network issues: Provide troubleshooting steps
• Permission issues: Suggest a different directory

2.3 Install from Source

cd "$TORCHTALK_DIR"
pip install -e .

Verify installation:

torchtalk --help

If install fails:

• Check Python version meets minimum requirements
• Check for missing system dependencies (libclang may need system packages)
• Display error message and suggest checking the TorchTalk README

If libclang fails to install:

libclang requires the clang development libraries.

Install:
- macOS: xcode-select --install (usually already present)
- Ubuntu/Debian: sudo apt install libclang-dev
- Fedora/RHEL: sudo dnf install clang-devel

Phase 3: Locate PyTorch Source

3.1 Check for Existing PyTorch Source

Search common locations:

echo "$PYTORCH_SOURCE"
ls -d ~/pytorch ~/src/pytorch /myworkspace/pytorch 2>/dev/null

If PyTorch source is found, confirm with the user:

Found PyTorch source at: /path/to/pytorch

Is this the correct PyTorch source directory? (y/n)

3.2 If Not Found, Ask the User

PyTorch source code is required for TorchTalk to index bindings.

Options:
1. Enter the path to an existing PyTorch checkout
2. Clone PyTorch now (requires ~2GB disk space, takes a few minutes)
3. Exit setup and clone manually

If cloning:

git clone https://github.com/pytorch/pytorch "$PYTORCH_CLONE_DIR"

Store the PyTorch path as $PYTORCH_SOURCE.

3.3 Validate PyTorch Source

Run checks separately so users get actionable error messages:

test -d "$PYTORCH_SOURCE/torch"

If the torch/ directory is missing:

No 'torch/' directory found — does not appear to be a PyTorch checkout.
Please verify the path and try again.

test -f "$PYTORCH_SOURCE/aten/src/ATen/native/native_functions.yaml"

If native_functions.yaml is missing:

native_functions.yaml not found (required for operator indexing).
The PyTorch checkout may be incomplete or too old.
Consider running: cd "$PYTORCH_SOURCE" && git pull

These checks mirror TorchTalk's validate_pytorch_path() which validates both the torch/ directory and native_functions.yaml.

Phase 4: Configure TorchTalk

4.1 Run TorchTalk Init

torchtalk init --pytorch-source "$PYTORCH_SOURCE"

This writes the PyTorch path to ~/.config/torchtalk/config.toml so future runs need no arguments.

Verify configuration:

torchtalk status

Confirm the output shows:

• Config file exists
• PyTorch source is valid
• Status is Ready

4.2 Register MCP Server with Claude Code

claude mcp add torchtalk -s user -- torchtalk mcp-serve

This registers TorchTalk at user scope, making it available across all Claude Code sessions regardless of working directory.

If claude command is not found:

The Claude Code CLI is required to register MCP servers.

If you're running this from within Claude Code, the registration
may need to be done from a terminal. Copy and run:

  claude mcp add torchtalk -s user -- torchtalk mcp-serve

Phase 5: Verify Setup

Run validation checks to confirm torchtalk status reports all green and mcp-serve starts without errors:

torchtalk status
timeout 5 torchtalk mcp-serve 2>&1 || true

Display summary:

Setup Complete!
===============

  TorchTalk installed at:  $TORCHTALK_DIR
  PyTorch source:          $PYTORCH_SOURCE
  Config file:             ~/.config/torchtalk/config.toml
  MCP server:              Registered at user scope

Next Steps:
-----------

1. Restart Claude Code to activate the MCP server

2. Verify MCP tools are available:
   Run: mcp__torchtalk__get_status

3. Trace your first operator:
   /torchtalk:trace matmul

Optional - Build PyTorch for full call graph support:
   cd $PYTORCH_SOURCE && python setup.py develop

Available Commands:
-------------------
- /torchtalk:trace <function> [focus]  -- Trace binding chains
- The TorchTalk Analyzer skill handles broader questions like
  "How does nn.Linear work?" or "What breaks if I change gemm?"

Documentation:
--------------
- TorchTalk: https://github.com/adabeyta/torchtalk

Phase 6: Optional Call Graph Setup

After the main setup is complete, inform the user about optional advanced features:

Optional: C++ Call Graph Support
================================

TorchTalk can analyze C++ function call relationships (impact analysis,
caller/callee tracking) if PyTorch has been built at least once.

This generates compile_commands.json which TorchTalk uses for
libclang-based static analysis.

Would you like to:
1. Skip for now (you can build later)
2. Learn how to build PyTorch

If the user wants build instructions:

Building PyTorch (one-time, takes 30-60 minutes):

  cd $PYTORCH_SOURCE
  python setup.py develop

After building, the call graph features (impact, calls, called_by)
will be automatically available on the next Claude Code session.

Check if compile_commands.json already exists:

test -f "$PYTORCH_SOURCE/build/compile_commands.json" && echo "found" || echo "not found"

If found: compile_commands.json already exists. Call graph features are available.

Return Value

• Success: Installation path, config file location, MCP server registration confirmation, and summary of next steps
• Partial Success: List of completed and failed steps with instructions to complete remaining setup manually
• Error: Detailed error message, step where failure occurred, and troubleshooting suggestions

Examples

1. Fresh install with existing PyTorch source:

   /torchtalk:setup
   
   > Python 3.12.1 detected
   > Installing TorchTalk to ~/src/torchtalk...
   > Found PyTorch source at ~/pytorch
   > Running torchtalk init...
   > Registering MCP server...
   
   Setup complete! Restart Claude Code to activate.
   

2. Fresh install, clone everything:

   /torchtalk:setup
   
   > Python 3.11.5 detected
   > Installing TorchTalk to ~/src/torchtalk...
   > Cloning PyTorch to ~/src/pytorch...
   > Running torchtalk init...
   > Registering MCP server...
   
   Setup complete! Restart Claude Code to activate.
   

3. Reconfigure existing installation:

   /torchtalk:setup
   
   > TorchTalk already installed at ~/src/torchtalk
   > Reconfigure? yes
   > Updating PyTorch source path...
   > Re-registering MCP server...
   
   Reconfiguration complete!
   

Error Handling

libclang Installation Failure

Scenario: pip install -e . fails on the libclang dependency.

Action:

libclang failed to install. This is usually a missing system package.

Install the clang development libraries:
- macOS: xcode-select --install
- Ubuntu/Debian: sudo apt install libclang-dev
- Fedora/RHEL: sudo dnf install clang-devel

Then retry: pip install -e .

PyTorch Source Too Old or Incomplete

Scenario: The PyTorch checkout is missing expected files.

Action:

The PyTorch source may be incomplete or too old.

Expected files not found:
- aten/src/ATen/native/native_functions.yaml

Try updating your PyTorch checkout:
  cd $PYTORCH_SOURCE && git pull

Or clone a fresh copy:
  git clone https://github.com/pytorch/pytorch

MCP Server Registration Failure

Scenario: claude mcp add command fails.

Action:

Failed to register MCP server with Claude Code.

You can register manually by running in a terminal:
  claude mcp add torchtalk -s user -- torchtalk mcp-serve

Or add to ~/.claude.json manually:
  {
    "mcpServers": {
      "torchtalk": {
        "command": "torchtalk",
        "args": ["mcp-serve"]
      }
    }
  }

See Also

• TorchTalk Repository
• PyTorch Source
• /torchtalk:trace - Trace PyTorch function binding chains
• TorchTalk Analyzer skill - Full MCP tools reference

Arguments

• This command takes no arguments. All configuration is collected interactively.