Tutorials Prompt Engineering Tutorial
Tool Calling — Complete Guide
Tool Calling — Complete Guide: free step-by-step lesson with examples, common mistakes, and interview tips — part of Prompt Engineering Tutorial on Toolliyo Academy.
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Prompt Engineering Tutorial · Lesson 35 of 100
Tool Calling
Prompts → Apps
Prompts · 1 — Basics · ~6 min · Module 4: Structured Outputs
What is this?
Tool calling is the runtime pattern: model requests a tool, platform runs it, feeds result back — covers functions, APIs, SQL, and browser actions.
Why should you care?
PromptVerse Tool Runtime sandboxes HTTP, SQL read-only, and Slack post behind RBAC.
See it live — copy this example
Copy the prompt into ChatGPT, Claude, or your LLM API playground and compare outputs.
onToolCall(call) {
if (!ALLOW.has(call.name)) throw new Error("denied");
if (call.name === "sql_read") return runReadOnlyQuery(call.args.sql);
if (call.name === "slack_post") return postSlack(call.args.channel, call.args.text);
}What happened?
- Handler checks allow-list before execution.
- sql_read uses read-only connection.
- Model never touches credentials directly.
Practice next
- List tools your agent needs.
- build one handler with auth check.
- Return JSON string to model.
- Add timeout 5s per tool.
- Log tool args for audit (redact PII).
Remember
Platform runs tools, not model. Allow-list + RBAC. Return structured tool results.
Slack notify agent
Incident bot posts summary channel.
Outcome: Tool runtime posts via scoped bot token.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
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