Agent Function Schemas & Tool Dispatch
How does converting natural language prompt instructions into structured JSON function declarations allow LLMs to invoke external tools?
Large language models do not execute code directly inside an API call; instead, function calling provides the model with structured schemas describing tool names, parameter types, descriptions, and required arguments. When a user prompt requires external data or action, the model outputs a structured tool call (such as a function name and JSON arguments) rather than freeform text. The host application executes that function and returns the structured result back to the model in a subsequent turn. This page provides a template compiler and deterministic dispatch mock illustrating how prompt bots translate into target schemas for Gemini, OpenAI, Claude, and OpenAPI.
This client-side demonstration parses preset templates or basic three-property fallbacks; it does not run an actual LLM, validate semantic parsing against custom prose prompts, or verify API endpoints in real time. The dispatch simulator displays hardcoded confidence scores (e.g., 98.4%) and mock string outputs without live inference. Furthermore, the telemetry metrics (such as -74% context overhead) are static visual UI badges rather than measured benchmarks.
Try a worked example
Select the 'SQL DB Query' preset chip. Notice that the Gem / Agent Name changes to 'SQL Query Generator & Explainer' and the Skill Identity updates to 'generate_sql_query'. Next, switch the 'Target Architecture Target' dropdown to 'Claude Tool Definition Schema'. The compiled JSON updates under the 'Generated Skill Schema' tab to output Claude's standard contract with top-level fields 'name', 'description', and 'input_schema' containing properties for 'natural_query', 'target_dialect', and 'include_explain_plan'.
Gemini Function Calling Architecture
In the Google Gemini API, tool declarations describe functions using a typed parameters object with properties and required fields. The model determines when to predict a function_call step rather than plain text, passing structured arguments for execution by the client application. Function calling with the Gemini API
The host application executes the function locally, wraps the return value in a function_result step, and supplies it back to the model across turns to complete the response. Function calling with the Gemini API
OpenAI and Anthropic Tool Contracts
OpenAI tool calling structures functions under a 'type': 'function' object containing 'name', 'description', and 'parameters' adhering to JSON Schema, with optional strict validation requiring explicit required properties. Function calling | OpenAI API
Anthropic Claude tool specifications define tools with 'name', 'description', and 'input_schema'. Claude returns a 'tool_use' block with an ID and parsed inputs, expecting the client to execute the operation and respond with a 'tool_result' block. Tool use with Claude - Claude Platform Docs
Sources and further reading