Schematic PDF to JSON

Precision-first conversion of vector PDF circuit schematics into an evidence-preserving SchematicIR JSON document. The project includes a Python conversion engine, command-line interface, MCP server, reusable Codex skill, and validation tools.

This project deliberately prefers missing uncertain content over emitting plausible but incorrect electrical information. A result marked needs_review must not be treated as a verified netlist.

Architecture

1. PDF parsing — extracts native text, vector paths, styles, coordinates, source IDs, and evidence.

2. Primitive recognition — identifies conservative wire, component, pin, junction, label, and network-flag candidates.

3. Connectivity recovery — applies explicit endpoint, T-junction, junction marker, and evidence-backed named-net rules.

4. JSON output — emits validated SchematicIR, unresolved issues, topology checks, and a deterministic hash.

Each conversion preserves all four stages as separate JSON artifacts.

Related MCP server: schematic-mcp-bridge

One-click agent installation

Codex plugin:

codex plugin marketplace add ght123247/schematic-pdf-to-json --ref v0.2.0
codex plugin add schematic-pdf-to-json@schematic-pdf-tools

Start a new Codex task after installation. The bundled plugin installs its isolated Python runtime automatically on the first MCP connection.

Claude Desktop, Cursor, and Gemini CLI on Windows:

irm https://raw.githubusercontent.com/ght123247/schematic-pdf-to-json/v0.2.0/install.ps1 | iex

Claude Desktop, Cursor, and Gemini CLI on macOS/Linux:

curl -fsSL https://raw.githubusercontent.com/ght123247/schematic-pdf-to-json/v0.2.0/install.sh | sh

The installer creates an isolated runtime under ~/.schematic-pdf-to-json, preserves unrelated settings, backs up every changed config, performs a real MCP handshake, and prints the tools it found. Python 3.11 or newer is the only prerequisite.

See Agent installation for selecting one agent, custom allowed roots, upgrades, uninstalling, and manual configuration.

Development install

Python 3.11 or newer is required.

python -m venv .venv
.\.venv\Scripts\Activate.ps1
python -m pip install -e ".[test]"

Command line

schematic-pdf inspect "D:\path\drawing.pdf"
schematic-pdf convert "D:\path\drawing.pdf" --output output\artifacts
schematic-pdf validate "D:\path\schematic.final.json"

The conversion output contains:

job-.../
├── layer1/raw.json
├── layer2/semantic.json
├── layer3/connectivity.json
├── layer4/schematic.final.json
└── manifest.json

Altium Designer PDF support

For native vector PDFs exported by Altium Designer, the recognizer selects an Altium-specific profile automatically. It handles Altium's dark-blue wiring, blue symbol graphics, black pin stubs, red/yellow sheet ports, adjacent reference/value text pairs, large multi-pin IC bodies, and exact cross-page named-net merging. Dense PCB-artwork pages embedded in a schematic PDF are excluded with a reviewable warning instead of being interpreted as a schematic.

The profile remains precision-first: unsupported or ambiguous values, pins, symbols, and labels are omitted from resolved JSON and retained as issues for manual review.

MCP server

Start the stdio server with:

schematic-pdf-mcp

Available tools:

• inspect_schematic_pdf

• convert_schematic_pdf

• validate_schematic_ir

The server exposes schematic://schema/current and staged artifacts through schematic://jobs/{job_id}/{stage}. Restrict readable local paths with SCHEMATIC_PDF_ALLOWED_ROOTS; set SCHEMATIC_PDF_ARTIFACT_ROOT to control the artifact destination.

Generic MCP configuration:

{
  "mcpServers": {
    "schematic-pdf": {
      "command": "schematic-pdf-mcp",
      "env": {
        "SCHEMATIC_PDF_ALLOWED_ROOTS": "D:\\schematics",
        "SCHEMATIC_PDF_ARTIFACT_ROOT": "D:\\schematic-output"
      }
    }
  }
}

Accuracy and validation

The final JSON contains source evidence and explicit issues for unresolved content. Schema validity and topology validity are independent of recognition completeness. Important policies include:

• no inferred connection at a four-way crossing without explicit evidence;

• no invented pin numbers, reference-designator suffixes, or net names;

• ambiguous part-number-like text is omitted and reported for review;

• recognition changes must keep 100% precision on registered real-PDF regression samples.

Run the automated suite:

python -m pytest

Run the EasyEDA-backed precision regression after placing authorized local sample PDFs and truth exports at the paths registered in the script:

python scripts\run_precision_regression.py

For a genuinely unseen PDF, lock the conversion before reading EDA truth:

python scripts\blind_validate_pdf.py lock "Test_PDF_SCH\new.pdf"
python scripts\blind_validate_pdf.py compare `
  "output\blind\job-...\blind-lock.json" `
  "output\accuracy\new.easyeda-ground-truth.json"

Test schematics, EasyEDA truth exports, and generated artifacts are excluded from the repository because they may contain proprietary circuit designs.

Codex skill

The reusable skill is in skill/convert-schematic-pdf. It defines the precision-first workflow, acceptance order, validation policy, MCP contract, and current limitations.

Current limitations

• Native vector PDFs are the primary supported input.

• Native Altium Designer schematic exports have a dedicated auto-detected recognition profile; custom print styles may still require calibration.

• Raster OCR and raster primitive recognition are not implemented.

• Hidden pin numbers and ambiguous symbol identity remain unresolved.

• Long-distance value/label association is intentionally conservative.

• Human review is still required whenever blocking issues remain.

License

MIT