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Control lab automation devices via MCP and test workflows in simulation.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "plr-mcp" yet — see the docs or source repo.
Using plr-mcp in simulation mode, run an end-to-end experiment workflow: dispense into a 96-well plate with the liquid handler, mix for 10 minutes with the heater-shaker, then read absorbance with the plate reader. Return each device call, parameters, and simulated results step by step.
A step-by-step execution log with device parameters, simulation status, and readout results.
Use plr-mcp to design and execute a PCR prep workflow: aliquot samples into a PCR plate, then run a specified thermocycler program. Output the full procedure, run parameters, and simulation success/failure status.
A reviewable PCR automation workflow including dispensing steps, thermocycler settings, and execution status.
I'm building a lab automation script. Use plr-mcp to simulate calls to the liquid handler and plate reader, check whether plate type, volume settings, and read mode are valid, and point out issues with recommended fixes.
A debugging-oriented parameter validation report highlighting issues, risks, and suggested fixes.
Simulate dynamic actions for any query with persistent state across sessions.
Let AI read and control industrial devices via standard industrial protocols.
Connect AI agents to control local JupyterLab for coding and analysis.
Connect and control robotics capabilities through an MCP server for AI workflows.
Predict reagent stockouts, manage lab inventory, and create orders from Slack.
Connects experiment platforms and adds local tools for analysis and skill debugging.