System Prompt & Tool-Calling Builder

Compose a structured agent system prompt and emit a function-calling tools array. Pick personas, goals, constraints and output contracts, then copy production-ready blocks.

Role / Persona

Goal

Context

Constraints (one per line)

Output Format

Tools (function calling)

0 tools selected

Generated System Prompt

…

Tools Array (JSON Schema)

…

Professional Insights & Guide

The anatomy of production system prompts and function-calling schemas, with real rules for stable agents.

Core Use Case scenario

An agent system prompt has five parts: the persona it adopts, the goal it optimizes for, the context it can assume, the constraints it must respect, and the output format it must obey. Function calling adds a sixth layer: a tools array, where each tool is a JSON object with a name, a description, and a parameters schema. The model reads these schemas and, when it needs external capability, emits a structured tool call instead of free text, which the host application executes and feeds back.

Troubleshooting & Edge-Case Failure Points

Vague personas invite scope creep: a role that is not tied to a measurable goal drifts off-task.
Unbounded tools: exposing every tool to every agent increases hallucinated calls and permission risk.
Missing output contract: free-form answers break parsers that expect strict Markdown, XML, or JSON.
Duplicate or conflicting tool names break schema validation and confuse the model's tool selection.

Detailed Step-by-Step Instructions

Enter a concrete role/persona and a measurable goal for the agent.
Add context the agent can rely on and one constraint per line to bound its behavior.
Choose the output format — Markdown, XML, or JSON — to fix the response contract.
Check the tools the agent may call; each selection contributes a full function schema.
Press Generate Prompt Blocks, review both outputs, and copy the system prompt, the tools array, or the full payload.

Schema Rules Used

{
  "type": "function",
  "function": {
    "name": "web_search",
    "description": "Search the web for up-to-date facts…",
    "parameters": {
      "type": "object",
      "properties": { "query": { "type": "string" } },
      "required": ["query"]
    }
  }
}
prompt_tokens  ≈ words * 1.33        // rough system prompt cost
format_contract: Markdown | XML | JSON

Related Web Utilities (Silo Hub)

Multi-Agent Topology Harness RAG Vector Chunking Simulator Prompt Chain Debugger Prompt Injection Guardrail ChatGPT Prompt Optimizer JSON Formatter & Validator

Informative Guides & Helper Articles

Common Errors in System Prompt Design

Vague personas, unbounded tool permissions, and output-format drift that breaks parsers.

Read Article →

How to Use the System Prompt & Tool-Calling Builder

Composes production agent prompts - persona, goal, context, constraints, output format - and emits JSON tool schemas for function calling.

Define the agent role, goal, hard constraints.
Add the output contract: format, length, refusal behavior.
Generate the prompt plus function-calling schemas.

System Prompts Are APIs

Identity, Capabilities, Rules, I/O contract, Examples

Treat the system prompt as an interface spec: sections in fixed order (identity, capabilities, rules, I/O contract, examples) test better than prose paragraphs. For tool-calling, the JSON schema is the UX: parameter descriptions get read literally - "zip_code: string" gets extensions; "5-digit US ZIP, no extension" gets compliance. Version prompts like code, keep a small eval set, and specify refusal/edge behavior - unspecified failures are the ones you get.