What is Cursor Ai Bpmn Translator

cursor-ai-bpmn-translator is a Model Context Protocol (MCP) component designed for Cursor AI that translates BPMN diagrams into human-readable descriptions and user stories.

Use cases

Use cases include automating the documentation of business processes, generating user stories for agile development, and enhancing communication between technical and non-technical stakeholders by providing clear process descriptions.

How to use

To use cursor-ai-bpmn-translator, you can operate it as a command line tool by running ‘python -m bpmn_translator translate path/to/your/diagram.bpmn’ or as an MCP component by importing the MCPBPMNTranslator class and calling its methods to translate BPMN content or generate user stories.

Key features

Key features include translating BPMN diagrams into clear process descriptions, generating user stories from BPMN processes, and supporting various BPMN elements such as events, tasks, sequence flows, participants, and documentation.

Where to use

cursor-ai-bpmn-translator can be used in various fields such as business process management, software development, and project management where BPMN diagrams are utilized to describe workflows.

BPMN Translator MCP for Cursor AI

A Model Context Protocol (MCP) component for Cursor AI that translates BPMN diagrams into human-readable descriptions and user stories.

Features

• Translate BPMN diagrams into clear, human-readable process descriptions
• Generate user stories from BPMN processes
• Support for various BPMN elements:
  • Events (start, end, intermediate, timer)
  • Tasks and Activities
  • Sequence Flows
  • Participants and Lanes
  • Documentation

Installation

pip install -r requirements.txt

Usage

As a Command Line Tool

python -m bpmn_translator translate path/to/your/diagram.bpmn

As an MCP Component

The BPMN Translator provides two main functions through its MCP interface:

1. translate_bpmn:

from bpmn_translator.mcp_adapter import MCPBPMNTranslator

# Translate BPMN content
result = MCPBPMNTranslator.translate_bpmn(
    bpmn_content="<your BPMN XML content>",
    output_format="both"  # Options: "description", "user_stories", "both"
)

# Access results
if "description" in result:
    print(result["description"])
if "user_stories" in result:
    print(result["user_stories"])

2. generate_user_stories:

from bpmn_translator.mcp_adapter import MCPBPMNTranslator

# Generate user stories for specific roles
stories = MCPBPMNTranslator.generate_user_stories(
    bpmn_content="<your BPMN XML content>",
    roles=["Business Customer", "System Administrator"]  # Optional
)

# Access user stories
print(stories["user_stories"])

Input Format

The component accepts BPMN 2.0 XML files. Example structure:

<?xml version="1.0" encoding="UTF-8"?>
<bpmn:definitions xmlns:bpmn="http://www.omg.org/spec/BPMN/20100524/MODEL">
  <bpmn:process id="Process_1">
    <bpmn:startEvent id="StartEvent_1" name="Start">
      <bpmn:outgoing>Flow_1</bpmn:outgoing>
    </bpmn:startEvent>
    <!-- ... more BPMN elements ... -->
  </bpmn:process>
</bpmn:definitions>

Output Format

Process Description

The process description provides a hierarchical view of the process flow:

The process can start in these ways:

Path:
- Start Process
  - First Task
    - Second Task
      - End Process

User Stories

User stories are generated in the format:

As a [role], I want to [action] so that [benefit]

Error Handling

The component includes robust error handling:

• BPMNTranslatorError: Base exception class
• BPMNParsingError: Raised when there’s an error parsing the BPMN file

Testing

Run the test suite:

pytest tests/

Contributing

1. Fork the repository
2. Create your feature branch
3. Commit your changes
4. Push to the branch
5. Create a new Pull Request

License

MIT License - see LICENSE file for details