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JSON Schema for Tool Calling
Core Concepts
- Definition: JSON Schema is a data validation specification—a set of rules used to validate any JSON data.
- Purpose in LLMs: While not exclusive to language models, the LLM community uses JSON Schema as a convenient, standardized way to define and handle tool calls.
- Tool Configuration Structure: The overall tool configuration object includes:
- A Name for the tool (e. g. , "get_weather").
- A Description that tells the LLM what the tool does, when to use it, and what data it returns.
- An Input Schema key, which contains the actual JSON Schema specification.
Best Practices for Descriptions
- Tool Description: Should be detailed (recommended length: 3 to 4 sentences) to help the LLM understand the tool's function and usage.
- Argument Description: The description within the input schema must clearly explain what the specific argument controls and how it affects the overall function call (recommended length: 3 to 4 sentences).
Generating Schemas
- Automated Generation: You can use a powerful LLM (like Claude) to generate a valid JSON schema spec for a tool function.
- Process: Prompt the LLM to create the schema, referencing best practices found in the official API documentation.
Coding Conventions
- Naming Convention: Use a consistent naming pattern for schemas, typically the tool function name followed by an underscore and "schema" (e. g. , get_current_date_time_schema).
- Type Safety: To prevent future type errors, wrap the entire schema dictionary using the ToolParam type from anthropic. types.
Takeaways
- JSON Schema is a data validation specification used to standardize and define tool calls for LLMs.
- A complete tool configuration requires a Name, a detailed Description, and an Input Schema.
- Descriptions for both the tool and its arguments should be detailed (recommended 3 to 4 sentences) to guide the LLM effectively.
- Schemas can be automatically generated using LLMs, but best practices include consistent naming and using type safety wrappers.
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