adfc8f4935
- Added a "Memories" section to document the retention of the current transport format for API requests. - Enhanced project documentation for better clarity on API behavior.
122 lines
3.6 KiB
Markdown
122 lines
3.6 KiB
Markdown
# CLAUDE.md
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This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
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## Project Overview
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Penpot MCP Server is a Python-based Model Context Protocol (MCP) server that bridges AI language models with Penpot, an open-source design platform. It enables programmatic interaction with design files through a well-structured API.
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## Key Commands
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### Development Setup
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```bash
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# Install dependencies (recommended)
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uv sync --extra dev
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# Run the MCP server
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uv run penpot-mcp
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# Run tests
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uv run pytest
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uv run pytest --cov=penpot_mcp tests/ # with coverage
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# Lint and fix code
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uv run python lint.py # check issues
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uv run python lint.py --autofix # auto-fix issues
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```
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### Running the Server
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```bash
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# Default stdio mode (for Claude Desktop/Cursor)
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make mcp-server
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# SSE mode (for debugging with inspector)
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make mcp-server-sse
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# Launch MCP inspector (requires SSE mode)
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make mcp-inspector
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```
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### CLI Tools
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```bash
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# Generate tree visualization
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penpot-tree path/to/penpot_file.json
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# Validate Penpot file
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penpot-validate path/to/penpot_file.json
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```
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## Architecture Overview
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### Core Components
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1. **MCP Server** (`penpot_mcp/server/mcp_server.py`)
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- Built on FastMCP framework
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- Implements resources and tools for Penpot interaction
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- Memory cache with 10-minute TTL
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- Supports stdio (default) and SSE modes
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2. **API Client** (`penpot_mcp/api/penpot_api.py`)
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- REST client for Penpot platform
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- Transit+JSON format handling
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- Cookie-based authentication with auto-refresh
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- Lazy authentication pattern
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3. **Key Design Patterns**
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- **Authentication**: Cookie-based with automatic re-authentication on 401/403
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- **Caching**: In-memory file cache to reduce API calls
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- **Resource/Tool Duality**: Resources can be exposed as tools via RESOURCES_AS_TOOLS config
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- **Transit Format**: Special handling for UUIDs (`~u` prefix) and keywords (`~:` prefix)
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### Available Tools/Functions
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- `list_projects`: Get all Penpot projects
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- `get_project_files`: List files in a project
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- `get_file`: Retrieve and cache file data
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- `search_object`: Search design objects by name (regex)
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- `get_object_tree`: Get filtered object tree with screenshot
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- `export_object`: Export design objects as images
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- `penpot_tree_schema`: Get schema for object tree fields
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### Environment Configuration
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Create a `.env` file with:
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```
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PENPOT_API_URL=https://design.penpot.app/api
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PENPOT_USERNAME=your_username
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PENPOT_PASSWORD=your_password
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ENABLE_HTTP_SERVER=true # for image serving
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RESOURCES_AS_TOOLS=false # MCP resource mode
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DEBUG=true # debug logging
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```
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### Working with the Codebase
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1. **Adding New Tools**: Decorate functions with `@self.mcp.tool()` in mcp_server.py
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2. **API Extensions**: Add methods to PenpotAPI class following existing patterns
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3. **Error Handling**: Always check for `"error"` keys in API responses
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4. **Testing**: Use `test_mode=True` when creating server instances in tests
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5. **Transit Format**: Remember to handle Transit+JSON when working with raw API
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### Common Workflow for Code Generation
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1. List projects → Find target project
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2. Get project files → Locate design file
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3. Search for component → Find specific element
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4. Get tree schema → Understand available fields
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5. Get object tree → Retrieve structure with screenshot
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6. Export if needed → Get rendered component image
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### Testing Patterns
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- Mock fixtures in `tests/conftest.py`
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- Test both stdio and SSE modes
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- Verify Transit format conversions
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- Check cache behavior and expiration
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## Memories
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- Keep the current transport format for the current API requests |