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Blueprint
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python
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6 months ago
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2 months ago
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Documentation & install
Readme and setup notes from the catalogue, plus a client-ready config you can copy for your MCP host.
Installation
Add the following to your MCP client configuration file.
Configuration
View docs{
"mcpServers": {
"arcadeai-blueprint-mcp": {
"command": "arcade-mcp",
"args": [
"deploy"
],
"env": {
"GOOGLE_API_KEY": "YOUR_API_KEY_PLACEHOLDER"
}
}
}
}Blueprint MCP enables you to visualize and automate codebase architectures by deploying an MCP server that generates diagrams from your data sources and code. It integrates with Arcade tools to create visual representations of system designs and workflows, helping you understand complex architectures and document them efficiently.
How to use
You run the MCP server from the Arcade command line interface and then access it through your MCP client to start diagram jobs, monitor progress, and retrieve diagrams. Use the server to analyze code, API flows, data pipelines, or any architecture you want to visualize. Start a job, check status, and download the resulting diagram in PNG format or base64 as needed.
How to install
Prerequisites you need before installation are Python, a shell environment, and the Arcade CLI. You will also use a Google AI Studio API key to enable advanced diagram generation.
# Create a Python virtual environment
python3 -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install Arcade MCP package
pip install arcade-mcp
Additional configuration and usage notes
Securely provide your Google AI Studio API key to the Arcade MCP workflow and store it as a secret for use during diagram generation.
arcade-mcp login
arcade-mcp secret set GOOGLE_API_KEY="your_api_key_here"
Available tools
start_diagram_job
Start a diagram generation job. Returns a job ID to track progress.
check_job_status
Check whether a diagram generation job is complete.
download_diagram
Download the generated diagram as a PNG, returned as base64.