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Google Gemini FunctionDeclaration & Type Schema Generator (@google/genai)

Build typed FunctionDeclaration schemas for Gemini 2.5 Pro and Flash with uppercase Type enums and parameter validation.

Generate production TypeScript declarations directly compatible with the official @google/genai SDK.

Function & Tool Schema Builder

Visually design agent tool arguments and generate code for Google Gemini, Anthropic Claude, and OpenAI.

Function Name (snake_case)

Tool Description (Instructs Model When & Why to Call)

Tool Parameters (3)

Req

import { FunctionDeclaration, Type } from '@google/genai';

export const execute_sql_queryDeclaration: FunctionDeclaration = {
  name: 'execute_sql_query',
  description: 'Executes a sandboxed, read-only SQL query against the read-replica data warehouse.',
  parameters: {
    type: Type.OBJECT,
    properties: {
    "query": {
        type: Type.STRING,
        "description": "The sanitized SELECT query to execute."
    },
    "limit": {
        type: Type.NUMBER,
        "description": "Maximum number of rows to return (default 100, max 1000)."
    },
    "format": {
        type: Type.STRING,
        "description": "Output response serialization format.",
        "enum": [
            "json",
            "csv",
            "markdown_table"
        ]
    }
},
    required: [
    "query"
],
  },
};

Enterprise Specification Matrix

Manual Approach vs. Governed CadreGPT Enterprise

Why organizations transition from unmanaged scripts to governed autonomous agent workspaces.

Side-by-side technical comparison for Google Gemini FunctionDeclaration & Type Schema Generator (@google/genai)
Feature Dimension	Manual / Unmanaged Way	Automated with CadreGPT Enterprise
Multi-Model Compatibility	Manually writing separate, incompatible JSON schemas for Gemini Type enums, Claude input_schema, and OpenAI tools.	Single visual definition exported seamlessly to Google Gemini, Anthropic Claude, and OpenAI function calling formats.
Credential Protection	Exposing database connection strings or third-party API keys directly inside agent prompt definitions.	AES-256-GCM envelope encryption where sandbox workers inject secrets into headers at runtime with zero LLM visibility.
Strict Parameter Validation	LLM generates malformed JSON arguments, resulting in unhandled application runtime crashes in production.	Deterministic Zod runtime validation checking parameter types, required fields, and enums before tool dispatch.
Execution Environment	Running arbitrary code or tool scripts directly on the host operating system without sandboxing.	Isolated ephemeral sandbox execution with strict network egress policies and human approval checkpoints.

Direct Answers for Humans & Search Engines

Frequently Asked Questions

Technical answers and operational best practices for Gemini SDK Tool Schema.

01.What is function calling in LLMs?

Function calling (or tool dispatch) enables large language models to output structured arguments conforming to a predefined JSON schema instead of plain text. The application executes the function with the model arguments and returns the result back to the agent.

02.How do schemas differ between Gemini, Claude, and OpenAI?

OpenAI uses the tools array with { type: "function", function: { parameters: { type: "object", ... } } }. Anthropic Claude uses tools with { name, input_schema: { type: "object", properties, required } }. Google Gemini uses FunctionDeclaration with uppercase Type enums (Type.STRING, Type.OBJECT). This tool translates between all three automatically.

03.Why is schema validation critical for autonomous agents?

LLMs are probabilistic and can occasionally hallucinate extra fields, omit required properties, or produce invalid types. Strict JSON schemas force the model attention heads to adhere to rigid structural boundaries.

04.Can I import and run these tools in CadreGPT?

Yes. In CadreGPT, you can register custom tool definitions with one click. Tools run in isolated sandboxes with automated AES-256 credential injection and optional human-in-the-loop review.