canforge-mcp

canforge-mcp is a local, read-only MCP server for inspecting DBC files and decoding CAN capture logs.

Files stay on the machine running the server. The server exposes bounded tools instead of uploading captures or returning unbounded traces.

Tools

See the tool reference for arguments, return shapes, and hard caps.

Related MCP server: telemetry-mcp

Install

Run directly with uvx:

uvx canforge-mcp

Or install with pip:

pip install canforge-mcp
canforge-mcp

Requires Python >=3.11.

Configure

Claude Code:

claude mcp add canforge -- uvx canforge-mcp

Claude Desktop (claude_desktop_config.json):

{
  "mcpServers": {
    "canforge": {
      "command": "uvx",
      "args": ["canforge-mcp"]
    }
  }
}

Restart Claude after changing its MCP configuration.

ChatGPT (Secure MCP Tunnel)

ChatGPT cannot start a local stdio MCP server directly. Use OpenAI's Secure MCP Tunnel to keep Canforge running locally without exposing it to the public internet.

Before starting, enable developer mode in ChatGPT under Settings → Security and login, then create a tunnel in the OpenAI Platform. You need its tunnel ID, a runtime API key, and the tunnel-client binary. Make sure the tunnel is associated with the ChatGPT workspace where you will use Canforge.

Configure and start the tunnel with placeholder credentials:

export CONTROL_PLANE_API_KEY="sk-..."

tunnel-client init \
  --sample sample_mcp_stdio_local \
  --profile canforge \
  --tunnel-id tunnel_your_id \
  --mcp-command "uvx canforge-mcp"

tunnel-client doctor --profile canforge --explain
tunnel-client run --profile canforge

Keep tunnel-client run running while using Canforge. In ChatGPT, open Settings → Plugins, add a developer-mode app, choose Tunnel as the connection, and select or paste the tunnel ID. Add the new app to a chat before asking ChatGPT to use the Canforge tools.

Canforge resolves paths on the machine running tunnel-client. Files attached directly to a ChatGPT conversation are not automatically available as local filesystem paths; provide an accessible local path instead.

Design

• Local-first: tools accept filesystem paths and do not send file content over the network.

• Read-only: no tool creates, edits, encodes, or overwrites a file.

• Bounded: list and frame tools enforce hard caps and report total, returned, and truncated; timeseries are downsampled server-side.

• Cached: parsed DBCs are cached by resolved path and nanosecond modification time for repeated inspection during one server session.

• Composable: capkit reads capture formats; dbckit parses and decodes DBC content.

• Stdio-only: the 0.x line exposes no network transport or hosted service.

Scope and Caveats

• Supported capture formats come from capkit 0.3: Kvaser CanKing TXT, candump text, and Vector ASC.

• DBC support and validation behavior follow dbckit 1.x.

• Timestamps are floats exactly as recorded by capkit; they are not rebased.

• Median cycle time is the median gap between consecutive occurrences of an ID.

• capkit adds raw priority, PGN, and source-address fields for observed extended IDs; dbckit remains responsible for DBC-aware J1939 matching and decoding.

• signal_timeseries uses deterministic, evenly spaced index sampling when a series exceeds max_points; it is intended for inspection, not resampling or signal processing.

• log_signal_inventory always loads the DBC leniently and reports parse diagnostics and per-message decode safety. It scans the log body once; include_values=true adds bounded distinct decoded values to the inventory.

• Paths are resolved by the machine running the MCP server. A remote client's filesystem is not visible to a server running elsewhere.

Development

python -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"
ruff check .
mypy canforge_mcp
pytest --cov=canforge_mcp --cov-fail-under=90
python -m build

Large-log performance checks are opt-in and generate their own deterministic captures. See benchmarks/README.md for the smoke command, the full 100k/1M matrix, measurement policy, and committed v0.2.0 baseline.

License

MIT