LangChain Tools Skill
Capabilities
- •Create custom LangChain tools with proper schemas
- •Integrate existing tools and APIs
- •Design tool descriptions for optimal LLM understanding
- •Implement structured tool inputs with Pydantic
- •Handle tool errors and fallbacks
- •Create tool chains and pipelines
Target Processes
- •custom-tool-development
- •function-calling-agent
Implementation Details
Tool Creation Patterns
- •@tool decorator: Simple function-based tools
- •StructuredTool: Tools with complex input schemas
- •BaseTool subclass: Full control over tool behavior
- •Tool from functions: Dynamic tool creation
Configuration Options
- •Tool name and description
- •Input schema (args_schema)
- •Return type specification
- •Error handling strategy
- •Async/sync execution modes
Best Practices
- •Clear, action-oriented descriptions
- •Explicit input parameter documentation
- •Proper error messages for LLM understanding
- •Idempotent operations where possible
Dependencies
- •langchain-core
- •pydantic