protocol-generation-from-description
Generate detailed laboratory protocols from natural language descriptions using AI, producing step-by-step experimental procedures ready for lab execution.
/plugin install InnoClawdetails
Laboratory Protocol Generation Workflow
Usage
1. MCP Server Definition
Use the same DrugSDAClient class pattern with Thoth-Plan server.
2. Protocol Generation from User Description
This workflow generates detailed laboratory protocols from natural language descriptions, useful for experimental planning and automation.
Workflow Steps:
- Input User Description - Provide natural language description of desired protocol
- Generate Detailed Protocol - AI generates step-by-step experimental procedure
- Optional: Convert to Executable Format - Transform protocol to machine-readable JSON for automation
Implementation:
client = DrugSDAClient("https://scp.intern-ai.org.cn/api/v1/mcp/19/Thoth-Plan")
if not await client.connect():
print("connection failed")
return
## Step 1: Provide protocol description
user_prompt = """
I need a PCR protocol for amplifying a 500bp DNA fragment.
Use a standard Taq polymerase with the following conditions:
- Annealing temperature: 55°C
- Extension time: 30 seconds
- 30 cycles total
Include primer concentrations and buffer composition.
"""
## Step 2: Generate detailed protocol
result = await client.session.call_tool(
"protocol_generation",
arguments={
"user_prompt": user_prompt
}
)
protocol_text = client.parse_result(result)["protocol"]
print("Generated Protocol:")
print("=" * 80)
print(protocol_text)
print("=" * 80)
## Step 3 (Optional): Convert to executable JSON for lab automation
result = await client.session.call_tool(
"generate_executable_json",
arguments={
"protocol": protocol_text
}
)
executable_json = client.parse_result(result)["executable_json"]
print("\nExecutable JSON for lab automation:")
print(executable_json)
## Step 4 (Optional): Execute protocol via lab automation system
result = await client.session.call_tool(
"execute_json",
arguments={
"executable_json": executable_json
}
)
execution_info = client.parse_result(result)
print("\nExecution Info:")
print(execution_info)
await client.disconnect()
Tool Descriptions
Thoth-Plan Server:
-
protocol_generation: Generate detailed laboratory protocol from description- Args:
user_prompt(str) - Natural language description of desired protocol - Returns:
protocol(str) - Detailed step-by-step protocol text
- Args:
-
generate_executable_json: Convert protocol text to machine-readable format- Args:
protocol(str) - Protocol text - Returns:
executable_json(str) - JSON format for Opentrons/lab automation
- Args:
-
execute_json: Execute protocol via connected lab automation systems- Args:
executable_json(str) - Executable protocol JSON - Returns: Execution status and log
- Args:
Input/Output
Input:
user_prompt: Natural language description of desired experimental protocol- Can include: reagents, conditions, equipment, expected outcomes
- Can reference standard protocols or specific parameters
Output:
protocol: Detailed step-by-step protocol including:- Materials and reagents list
- Equipment requirements
- Detailed procedure steps
- Safety considerations
- Expected results
- Troubleshooting tips
Example Protocol Types
The system can generate protocols for various laboratory procedures:
- Molecular Biology: PCR, cloning, gel electrophoresis, DNA extraction, transformation
- Protein Science: Protein purification, Western blot, ELISA, protein crystallization
- Cell Culture: Cell passage, transfection, differentiation, cryopreservation
- Biochemistry: Enzyme assays, metabolite extraction, chromatography
- Analytical: Spectroscopy, mass spectrometry sample prep, HPLC
Protocol Quality Guidelines
Generated protocols include:
- ✓ Precise volumes and concentrations
- ✓ Specific temperatures and times
- ✓ Safety warnings where applicable
- ✓ Quality control checkpoints
- ✓ Troubleshooting guidance
Integration with Lab Automation
The generated protocols can be converted to executable JSON format compatible with:
- Opentrons liquid handling robots
- Hamilton automated workstations
- Custom lab automation systems
- Electronic lab notebooks (ELNs)
Best Practices
For optimal protocol generation:
- Be Specific: Include target specifications (e.g., "500bp fragment", "55°C annealing")
- Mention Equipment: Specify if using particular instruments or kits
- State Goals: Describe the experimental objective
- Include Constraints: Note any limitations (time, budget, available reagents)
- Reference Standards: Mention if following particular methods or publications
Example Good Prompts:
"Generate a Western blot protocol for detecting GAPDH (37 kDa) in HEK293 cell lysates using a standard semi-dry transfer system"
"I need a DNA extraction protocol from plant tissue (Arabidopsis leaves) optimized for downstream PCR. Yield target is 50 µg from 100mg tissue"
"Create a protein purification protocol for His-tagged recombinant protein from E. coli using IMAC chromatography. Starting culture volume is 500mL"
Limitations
- Generated protocols should be reviewed by qualified personnel before execution
- May require adjustment based on specific lab equipment and reagents
- Safety protocols should be verified against institutional guidelines
- Novel or untested procedures may need optimization
technical
- github
- SpectrAI-Initiative/InnoClaw
- stars
- 374
- license
- Apache-2.0
- contributors
- 16
- last commit
- 2026-04-20T01:27:21Z
- file
- .claude/skills/protocol-generation/SKILL.md