mirror of
https://github.com/mims-harvard/ToolUniverse.git
synced 2026-09-19 07:31:47 +08:00
Shanghua Gao
9f7070ed7b
### **Visualization Suite** - 2D/3D molecular visualization with interactive HTML output - Protein structure rendering with multiple visualization styles - Scientific plotting with Plotly, Matplotlib, and NetworkX ### **Coding API** - Type-safe function calling: `from tooluniverse.tools import ToolName` - Dynamic tool access: `tu.tools.ToolName()` - Built-in caching and error handling ### **Health Check System** - New `tooluniverse-doctor` CLI tool - Automatic dependency validation and bulk fix commands ### **Expanded Tool Ecosystem** - 50+ new scientific tools - Clinical guidelines (NICE, WHO, Europe PMC)
111 lines
4.3 KiB
Python
111 lines
4.3 KiB
Python
#!/usr/bin/env python3
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"""
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ToolUniverse Parameter Passing Example
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Demonstrates how tools receive execution context parameters
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"""
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from tooluniverse import ToolUniverse
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from tooluniverse.base_tool import BaseTool
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from tooluniverse.tool_registry import register_tool
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# =============================================================================
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# Custom Tool Definition
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# =============================================================================
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# Description: Create a demonstration tool that can respond to execution parameters
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# Syntax: @register_tool decorator with config
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@register_tool('DemoTool', config={
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"name": "demo_tool",
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"type": "DemoTool",
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"description": "Demonstration tool",
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"parameter": {
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"type": "object",
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"properties": {"message": {"type": "string"}},
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"required": ["message"]
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}
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})
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class DemoTool(BaseTool):
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def run(self, arguments=None, stream_callback=None, use_cache=False, validate=True):
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message = arguments.get("message", "")
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# Adjust behavior based on execution parameters
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mode = "cached" if use_cache else "realtime"
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status = "validated" if validate else "unvalidated"
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# Stream output callback for real-time progress updates
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if stream_callback:
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stream_callback(f"Processing: {message}\n")
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return f"{mode} - {message} ({status})"
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# =============================================================================
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# Method 1: Basic Parameter Passing
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# =============================================================================
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# Description: Default parameter usage
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# Syntax: tu.tools.tool_name(parameter=value)
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tu = ToolUniverse()
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tu.load_tools()
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# Basic calling - default parameters
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result1 = tu.tools.demo_tool(message="Hello")
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# =============================================================================
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# Method 2: Caching Parameter
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# =============================================================================
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# Description: Enable caching for repeated calls
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# Syntax: tu.tools.tool_name(parameter=value, use_cache=True)
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# Using cache - second call will be faster
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result2 = tu.tools.demo_tool(message="World", use_cache=True)
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# =============================================================================
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# Method 3: Validation Parameter
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# =============================================================================
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# Description: Skip validation to improve performance
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# Syntax: tu.tools.tool_name(parameter=value, validate=False)
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# Skip validation - improve performance
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result3 = tu.tools.demo_tool(message="Test", validate=False)
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# =============================================================================
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# Method 4: Streaming Parameter
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# =============================================================================
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# Description: Real-time progress display with callback
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# Syntax: tu.tools.tool_name(parameter=value, stream_callback=callback)
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# Stream output - real-time progress display
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def callback(chunk):
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# Process streaming output chunks
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pass
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result4 = tu.tools.demo_tool(message="Stream", stream_callback=callback)
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# =============================================================================
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# Method 5: All Parameters Combined
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# =============================================================================
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# Description: Use all parameter combinations for complete functionality
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# Syntax: tu.tools.tool_name(param=value, stream_callback=cb, use_cache=True, validate=True)
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# All parameter combinations - complete functionality demonstration
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result5 = tu.tools.demo_tool(
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message="All",
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stream_callback=callback,
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use_cache=True,
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validate=True
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)
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# =============================================================================
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# Summary of Parameters
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# =============================================================================
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# Tools can access these execution context parameters:
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# - arguments: Tool-specific parameters passed by user
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# - stream_callback: Function for real-time progress updates
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# - use_cache: Whether results should be cached
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# - validate: Whether input validation should be performed
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#
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# These parameters allow tools to:
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# - Adapt behavior based on execution context
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# - Provide streaming output when needed
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# - Optimize performance based on caching settings
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# - Perform additional validation when needed |