Bambuddy MCP Server
Enables AI assistants to interact with Bambuddy's 3D printer management API, including printer status, print queue, filament spools, and camera snapshots.
README
Bambuddy MCP Server
An MCP server that exposes the full Bambuddy REST API as tools for AI assistants.
This MCP server dynamically generates tools from Bambuddy's OpenAPI spec at startup, giving your AI assistant access to 430+ API endpoints — without flooding the context window on startup.
How It Works
On startup, the server fetches the OpenAPI spec from your running Bambuddy instance (/openapi.json), parses all 430+ endpoints, and indexes them by category.
By default, only 3 meta-tools are registered with the AI assistant:
| Meta-tool | Purpose |
|---|---|
list_categories |
Browse available API categories |
search_tools |
Find tools by keyword (with fuzzy matching) |
execute_tool |
Call any discovered tool by name |
This keeps the context window small while still providing full API coverage. The AI searches for what it needs, inspects the input schema, and executes — all on demand.
When a tool is called, the server makes the corresponding HTTP request to Bambuddy and returns the response. JSON responses are returned as text, while binary responses (e.g. camera snapshots) are returned as native MCP ImageContent with base64-encoded data so AI assistants can see, process, and display them directly.
Example Usage
Once configured, you can ask your AI assistant things like:
- "What printers are connected?"
- "Show me the status of my A1 Mini"
- "List my recent print archives"
- "Add the benchy to the print queue"
- "What filament spools do I have?"
- "Check the print progress"
- "Turn on the chamber light"
- "Show me a camera snapshot from printer X"
Requirements
- Python 3.10+
- uv — install with
curl -LsSf https://astral.sh/uv/install.sh | sh - A running Bambuddy instance
Installation
uv pip install bambuddy-mcp
Or install from source:
git clone https://github.com/maziggy/bambuddy-mcp.git
cd bambuddy-mcp
uv sync
Configuration
Using uvx
{
"mcpServers": {
"bambuddy": {
"command": "uvx",
"args": ["bambuddy-mcp"],
"env": {
"BAMBUDDY_URL": "http://localhost:8000",
"BAMBUDDY_API_KEY": "your-api-key",
"BAMBUDDY_CENSOR_ACCESS_CODE": "true",
"BAMBUDDY_CENSOR_SERIAL": "true",
"BAMBUDDY_CENSOR_MODEL_FILENAME": "false"
}
}
}
}
Local development
For development or running from source:
{
"mcpServers": {
"bambuddy": {
"command": "uv",
"args": ["run", "--directory", "/path/to/bambuddy-mcp", "python", "-m", "bambuddy_mcp"],
"env": {
"BAMBUDDY_URL": "http://localhost:8000",
"BAMBUDDY_API_KEY": "your-api-key",
"BAMBUDDY_CENSOR_ACCESS_CODE": "true",
"BAMBUDDY_CENSOR_SERIAL": "true",
"BAMBUDDY_CENSOR_MODEL_FILENAME": "false"
}
}
}
}
NixOS
On NixOS, use the system Python to avoid dynamic linking issues:
{
"mcpServers": {
"bambuddy": {
"command": "nix-shell",
"args": [
"-p", "uv",
"--run", "UV_PYTHON=/run/current-system/sw/bin/python3 uv --directory /path/to/bambuddy-mcp run bambuddy-mcp"
],
"env": {
"BAMBUDDY_URL": "http://localhost:8000",
"BAMBUDDY_API_KEY": "your-api-key"
}
}
}
}
Environment Variables
| Variable | Default | Description |
|---|---|---|
BAMBUDDY_URL |
http://localhost:8000 |
Base URL of your Bambuddy instance |
BAMBUDDY_API_KEY |
(empty) | API key for authentication (create in Bambuddy Settings) |
BAMBUDDY_DIRECT_MODE |
false |
Set to true to expose all 430+ tools directly instead of the meta-tools |
BAMBUDDY_CENSOR_ACCESS_CODE |
true |
Mask access_code fields in API responses |
BAMBUDDY_CENSOR_SERIAL |
true |
Mask serial_number fields (keeps first 2 + last 2 chars) |
BAMBUDDY_CENSOR_MODEL_FILENAME |
false |
Mask model filenames (.3mf, .gcode) in API responses and prevent direct base64 image embedding |
Note: By default, the server exposes meta-tools (
list_categories,search_tools,execute_tool,find_printer) that let AI assistants discover and call API endpoints on demand. SetBAMBUDDY_DIRECT_MODE=trueto expose all 430+ tools directly (uses significantly more context).
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.