Opentrons▌
by yerbymatey
Integrate Opentrons with leading lab automation systems for seamless control of liquid handling robots and laboratory au
Integrates with Opentrons laboratory robots to enable natural language control of protocol upload, run management, hardware operations, and system monitoring for both OT-2 and Flex platforms.
Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.
best for
- / Laboratory researchers automating protocols
- / Developers integrating with Opentrons robots
- / Lab managers monitoring robot operations
capabilities
- / Upload and manage laboratory protocols
- / Start, stop and monitor robot runs
- / Control robot hardware (homing, lights, basic operations)
- / Search Opentrons API endpoints and documentation
- / Monitor robot health and connectivity status
- / Browse API endpoints by category
what it does
Connects to Opentrons laboratory robots for natural language control of protocols, runs, and hardware operations. Includes comprehensive API documentation tools for developers.
about
Opentrons is a community-built MCP server published by yerbymatey that provides AI assistants with tools and capabilities via the Model Context Protocol. Integrate Opentrons with leading lab automation systems for seamless control of liquid handling robots and laboratory au It is categorized under developer tools.
how to install
You can install Opentrons in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
license
MIT
Opentrons is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
readme
Opentrons MCP Server
A Model Context Protocol (MCP) server for Opentrons robot automation and API documentation. This tool provides both comprehensive API documentation and direct robot control capabilities for Opentrons Flex and OT-2 robots.
Features
API Documentation Tools
- Search Endpoints: Find API endpoints by functionality, method, or keyword
- Endpoint Details: Get comprehensive information about specific API endpoints
- Category Browsing: List endpoints by functional category
- API Overview: High-level overview of the entire Opentrons HTTP API
Robot Automation Tools
- Protocol Management: Upload, list, and manage protocol files
- Run Control: Create runs, start/stop execution, monitor progress
- Robot Health: Check connectivity and system status
- Hardware Control: Home robot, control lights, and basic operations
Installation
From npm (recommended)
npm install -g opentrons-mcp
From source
git clone https://github.com/yerbymatey/opentrons-mcp.git
cd opentrons-mcp
npm install
Configuration
Add to your Claude Desktop configuration file:
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
{
"mcpServers": {
"opentrons": {
"command": "opentrons-mcp",
"args": []
}
}
}
If installed from source:
{
"mcpServers": {
"opentrons": {
"command": "node",
"args": ["/path/to/opentrons-mcp/index.js"]
}
}
}
Available Tools
Documentation Tools
search_endpoints
Search Opentrons HTTP API endpoints by functionality, method, path, or keyword.
query(required): Search termmethod(optional): Filter by HTTP method (GET, POST, PUT, DELETE, PATCH)tag(optional): Filter by API categoryinclude_deprecated(optional): Include deprecated endpoints
get_endpoint_details
Get comprehensive details about a specific API endpoint.
method(required): HTTP methodpath(required): API endpoint path
list_by_category
List all endpoints in a specific functional category.
category(required): API category (Health, Control, Protocol Management, etc.)
get_api_overview
Get high-level overview of the Opentrons HTTP API structure and capabilities.
Automation Tools
upload_protocol
Upload a protocol file to an Opentrons robot.
robot_ip(required): Robot IP addressfile_path(required): Path to protocol file (.py or .json)protocol_kind(optional): "standard" or "quick-transfer" (default: "standard")key(optional): Client tracking keyrun_time_parameters(optional): Runtime parameter values
get_protocols
List all protocols stored on the robot.
robot_ip(required): Robot IP addressprotocol_kind(optional): Filter by protocol type
create_run
Create a new protocol run on the robot.
robot_ip(required): Robot IP addressprotocol_id(required): ID of protocol to runrun_time_parameters(optional): Runtime parameter values
control_run
Control run execution (play, pause, stop, resume).
robot_ip(required): Robot IP addressrun_id(required): Run ID to controlaction(required): "play", "pause", "stop", or "resume-from-recovery"
get_runs
List all runs on the robot.
robot_ip(required): Robot IP address
get_run_status
Get detailed status of a specific run.
robot_ip(required): Robot IP addressrun_id(required): Run ID to check
robot_health
Check robot health and connectivity.
robot_ip(required): Robot IP address
control_lights
Turn robot lights on or off.
robot_ip(required): Robot IP addresson(required): true to turn lights on, false to turn off
home_robot
Home robot axes or specific pipette.
robot_ip(required): Robot IP addresstarget(optional): "robot" for all axes, "pipette" for specific mountmount(optional): "left" or "right" (required if target is "pipette")
Usage Examples
With Claude Desktop
Screenshot showing the Opentrons MCP server in action with Claude Desktop after asking for current protocols with opentrons for the Flex, give it the robot ip!
Once configured, you can use natural language to control your robot:
Upload a protocol:
Upload the protocol file at /path/to/my_protocol.py to my robot at 192.168.1.100
Check robot status:
Check if my robot at 192.168.1.100 is healthy and ready
Run a protocol:
List all protocols on my robot, then create and start a run for the latest one
Monitor progress:
Show me the status of run abc123 on my robot
Programmatic Usage
import { Client } from "@modelcontextprotocol/sdk/client/index.js";
// Connect to MCP server
const client = new Client(/* transport */);
// Upload protocol
await client.request({
method: "tools/call",
params: {
name: "upload_protocol",
arguments: {
robot_ip: "192.168.1.100",
file_path: "/path/to/protocol.py",
protocol_kind: "standard"
}
}
});
Requirements
- Node.js 18+
- Opentrons robot with HTTP API enabled (port 31950)
- Network connectivity between client and robot
Robot Setup
Ensure your Opentrons robot is:
- Connected to the same network as your client
- Running robot software version 7.0.0+
- Accessible on port 31950 (default for HTTP API)
You can verify connectivity by visiting http://your-robot-ip:31950/health in a browser.
API Reference
This tool provides access to the complete Opentrons HTTP API, including:
- Protocol Management: Upload, analyze, and manage protocol files
- Run Management: Create, control, and monitor protocol runs
- Hardware Control: Robot movement, homing, lighting, and calibration
- System Management: Health monitoring, settings, and diagnostics
- Module Control: Temperature modules, magnetic modules, thermocyclers
- Data Management: CSV files for runtime parameters
For detailed API documentation, use the search and documentation tools provided by this MCP server.
Troubleshooting
Cannot connect to robot
- Verify robot IP address is correct
- Ensure robot is powered on and connected to network
- Check that port 31950 is accessible
- Confirm robot software is running
Protocol upload fails
- Verify file path exists and is readable
- Ensure protocol file is valid Python (.py) or JSON format
- Check available disk space on robot
- Confirm protocol is compatible with robot type (OT-2 vs Flex)
Run execution issues
- Verify all required labware and modules are attached
- Check robot calibration status
- Ensure protocol analysis completed successfully
- Confirm no hardware errors or conflicts
Contributing
Contributions are welcome! Please feel free to submit issues and pull requests.
License
No license go brazy
Related Projects
FAQ
- What is the Opentrons MCP server?
- Opentrons is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
- How do MCP servers relate to agent skills?
- Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
- How are reviews shown for Opentrons?
- This profile displays 51 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.4 out of 5—verify behavior in your own environment before production use.
Use Cases▌
Extended AI Capabilities
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Context Enhancement
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Workflow Automation
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Implementation Guide▌
Prerequisites
- ›Claude Desktop 0.7.0+ or Cursor IDE with MCP support
- ›Basic understanding of MCP architecture and capabilities
- ›Access credentials for integrated services (if required)
- ›Willingness to experiment and iterate on configuration
Time Estimate
15-60 minutes depending on server complexity
Installation Steps
- 1.Install MCP server: npm install -g [package-name] or via GitHub
- 2.Add server configuration to ~/.claude/mcp.json
- 3.Provide required credentials and configuration
- 4.Restart Claude Desktop to load new server
- 5.Test basic functionality with simple prompts
- 6.Explore capabilities and experiment with use cases
- 7.Document successful patterns for reuse
Troubleshooting
- ⚠MCP server not loading: Check config syntax, verify installation
- ⚠Connection errors: Check network, firewall, credentials
- ⚠Feature not working: Read server docs, check required parameters
- ⚠Performance issues: Monitor resource usage, check for network latency
- ⚠Conflicts with other servers: Check port assignments, namespace collisions
Best Practices▌
✓ Do
- +Read server documentation thoroughly before setup
- +Start with simple use cases to validate functionality
- +Test in non-production environment first
- +Monitor resource usage and performance
- +Keep servers updated for bug fixes and new features
- +Document configuration for team members
- +Use environment variables for sensitive configuration
✗ Don't
- −Don't grant overly permissive access to MCP servers
- −Don't skip reading security considerations in docs
- −Don't expose sensitive data without proper controls
- −Don't run untrusted MCP servers without code review
- −Don't ignore error messages—investigate root cause
💡 Pro Tips
- ★Combine multiple MCP servers for powerful workflows
- ★Create custom MCP servers for your specific needs
- ★Share successful configurations with team
- ★Use MCP inspector for debugging
- ★Join MCP community for tips and troubleshooting
Technical Details▌
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
- Model Context Protocol (MCP)
- JSON-RPC 2.0
- stdio or HTTP transport
Compatibility
- Claude Desktop
- Cursor IDE
- Custom MCP clients
When to Use This▌
✓ Use When
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid When
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.
Integration▌
- →Tool composition: Chain multiple MCP tools in workflows
- →Context augmentation: Provide AI with relevant external data
- →Action delegation: Let AI execute tasks on external systems
- →Bidirectional sync: Keep AI context and external systems in sync
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
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Ratings
4.4★★★★★51 reviews- ★★★★★Benjamin Malhotra· Dec 28, 2024
I recommend Opentrons for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
- ★★★★★Mia Haddad· Dec 28, 2024
Strong directory entry: Opentrons surfaces stars and publisher context so we could sanity-check maintenance before adopting.
- ★★★★★Chaitanya Patil· Dec 20, 2024
We wired Opentrons into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
- ★★★★★Mia Sethi· Dec 20, 2024
Opentrons is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
- ★★★★★Arjun Agarwal· Dec 16, 2024
We evaluated Opentrons against two servers with overlapping tools; this profile had the clearer scope statement.
- ★★★★★Benjamin Khanna· Dec 16, 2024
Opentrons is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Michael Taylor· Nov 27, 2024
We evaluated Opentrons against two servers with overlapping tools; this profile had the clearer scope statement.
- ★★★★★Piyush G· Nov 11, 2024
Strong directory entry: Opentrons surfaces stars and publisher context so we could sanity-check maintenance before adopting.
- ★★★★★Hiroshi Haddad· Nov 11, 2024
Opentrons is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Evelyn Flores· Nov 7, 2024
Opentrons is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
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