appkit-bpmn-server
An MCP server that generates, edits, and manages BPMN 2.0 diagrams from natural language descriptions, with an interactive viewer and persistent storage.
README
appkit-bpmn-server
An MCP Apps-compliant server that generates, edits, and manages BPMN 2.0 diagrams from natural language — powered by LLMs.
Describe a business process in plain text and get a valid, layouted BPMN diagram with an interactive viewer rendered inline in any MCP Apps-capable host (Claude, ChatGPT, VS Code, etc.).

Features
- Natural language to BPMN — Generate process diagrams from text descriptions
- Interactive viewer — Built-in bpmn-js editor with zoom, pan, and editing capabilities
- Update diagrams — Modify existing diagrams via follow-up prompts without starting over
- Persistent storage — SQLite-backed diagram storage with Alembic migrations
- User assignment — Associate diagrams with specific users via
x-user-idheader or query parameter (defaults to-1) - Auto-layout — Automatic swimlane arrangement and edge routing
- Validation — Generated BPMN XML is validated before storage
- MCP Apps UI — Inline rendering in any compliant chat host
Architecture
MCP Host (Claude, ChatGPT, …)
│
│ MCP over HTTP (stateless)
▼
appkit-bpmn-server ← this repo (configuration + startup)
│
├─ appkit-mcp-bpmn ← MCP tools, viewer, BPMN generation
├─ appkit-commons ← shared config, OpenAI client, registry
└─ SQLite database ← diagram persistence
Prerequisites
- Python 3.14+
- uv package manager
- An OpenAI-compatible API key (Azure OpenAI or OpenAI)
Getting Started
1. Clone and install
git clone https://github.com/jenreh/appkit-bpmn-server.git
cd appkit-bpmn-server
uv sync
2. Configure environment
Create a .env file in the project root:
OPENAI_API_KEY=your-api-key
OPENAI_BASE_URL=https://your-endpoint.openai.azure.com/v1
3. Run database migrations
uv run alembic upgrade head
4. Start the server
uv run appkit_bpmn_server
The MCP endpoint will be available at http://127.0.0.1:8000/mcp.
5. Connect from an MCP client
Add the server to your MCP client configuration:
{
"mcpServers": {
"bpmn": {
"url": "http://127.0.0.1:8000/mcp"
}
}
}
MCP Tools
| Tool | Description |
|---|---|
new_bpmn_diagram |
Generate a new BPMN diagram from a natural language description |
update_bpmn_diagram |
Modify an existing diagram using a follow-up prompt |
save_bpmn_diagram |
Save pre-built BPMN XML |
get_bpmn_xml |
Retrieve the BPMN XML for a diagram (app-only) |
save_or_update |
Persist edits made in the viewer (app-only) |
rename_bpmn_diagram |
Rename a diagram (app-only) |
Tools marked app-only are hidden from the model and callable only from the interactive viewer.
Configuration
Server settings are in configuration/config.yaml:
app:
mcp_bpmn:
storage_mode: database # database, filesystem, or both
default_model: gpt-5.3-codex # LLM model for generation
max_file_size_mb: 10
diagram_types:
- process
User Assignment
Diagrams can be associated with a specific user by sending an x-user-id value. This can be provided as:
- HTTP header:
x-user-id: 123 - Query parameter:
?x-user-id=123
If neither is provided, the user ID defaults to -1.
Project Structure
├── configuration/
│ ├── config.yaml # Development configuration
│ ├── config.prod.yaml # Production overrides
│ └── logging.yaml # Logging configuration
├── alembic/ # Database migrations
├── src/
│ └── appkit_bpmn_server/
│ └── main.py # Server entry point
├── pyproject.toml
└── .env # API keys (not committed)
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.