---
id: wiki-2026-0508-tool-usage-optimization
title: Tool Usage Optimization
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Function Calling, Tool Use, Agent Tools]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [llm, tool-use, function-calling, agentic]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: Python
  framework: Anthropic SDK / OpenAI SDK / MCP
---

# Tool Usage Optimization

## 매 한 줄
> **"매 LLM의 외부 capability 의 invoke"**. Tool use (function calling) 매 LLM 이 external API/code/DB 의 structured call 하는 패러다임. 2023 OpenAI function calling → 2024 parallel tools → 2025 MCP standard → 2026 매 every agent 의 backbone. 매 quality 매 tool 정의의 crisp / parallel call / error handling / cache strategy 에 의해 결정.

## 매 핵심

### 매 핵심 mechanics
- **Schema** — 매 JSON Schema 로 tool input 정의 — 매 model 이 fill.
- **Choice** — `auto` / `tool` (forced) / `none`.
- **Parallel** — 매 single turn 에 multiple tool calls (Claude / GPT-4o+).
- **Iterative loop** — 매 tool result 의 feed back → continue → repeat.

### 매 design principles
- **Crisp descriptions** — 매 tool description 매 "use this when X, not when Y" 명시.
- **Few well-named tools** > 매 many overlapping — 매 selection error rate 의 reduce.
- **Idempotent / safe** — 매 retry-safe — 매 LLM 매 retry 함.
- **Structured errors** — 매 tool error 매 LLM 이 recover 할 수 있게 actionable.

### 매 modern (2025-2026)
- **MCP (Model Context Protocol)** — 매 Anthropic 이 2024-11 release. 매 client/server tool sharing standard. 매 Claude Desktop, Cursor, Windsurf 모두 support.
- **Tool result caching** — 매 expensive tool (DB query, web fetch) 매 cache + hash check.
- **Tool budget** — 매 agent 의 max-call limit 의 prevent runaway.

### 매 응용
1. Code agents (file ops, shell, search).
2. Customer support (ticket, KB, account API).
3. Data analysis (SQL, plot, fetch).

## 💻 패턴

### Anthropic tool definition
```python
tools = [{
    "name": "get_weather",
    "description": "Get current weather. Use ONLY for weather; do not use for forecasts beyond 24h.",
    "input_schema": {
        "type": "object",
        "properties": {
            "location": {"type": "string", "description": "City, country (e.g. 'Seoul, KR')"},
            "unit": {"type": "string", "enum": ["c", "f"], "default": "c"},
        },
        "required": ["location"],
    },
}]

resp = client.messages.create(
    model="claude-opus-4-7",
    max_tokens=2000,
    tools=tools,
    messages=[{"role": "user", "content": "Weather in Seoul?"}],
)
```

### Parallel tool execution
```python
import asyncio

async def run_tool(call):
    return await TOOLS[call.name](**call.input)

# 매 LLM이 매 multiple tool_use blocks 의 emit
calls = [b for b in resp.content if b.type == "tool_use"]
results = await asyncio.gather(*(run_tool(c) for c in calls))
# 매 모든 result 의 single user message 로 feed back
```

### Tool loop (agentic)
```python
messages = [{"role": "user", "content": user_query}]
for _ in range(MAX_TURNS):
    resp = client.messages.create(model="claude-opus-4-7", tools=tools, messages=messages)
    messages.append({"role": "assistant", "content": resp.content})
    if resp.stop_reason != "tool_use":
        break
    tool_results = [
        {"type": "tool_result", "tool_use_id": b.id,
         "content": str(TOOLS[b.name](**b.input))}
        for b in resp.content if b.type == "tool_use"
    ]
    messages.append({"role": "user", "content": tool_results})
```

### Structured error
```python
def search_db(query: str):
    try:
        return {"ok": True, "rows": db.execute(query).fetchall()}
    except SQLError as e:
        return {"ok": False, "error": str(e),
                "hint": "Check column names; use `\\d table` to inspect."}
```

### Result caching
```python
import hashlib, json, functools

@functools.lru_cache(maxsize=1024)
def _cached_fetch(url_hash: str, url: str):
    return requests.get(url, timeout=10).text

def web_fetch(url: str):
    return _cached_fetch(hashlib.sha256(url.encode()).hexdigest(), url)
```

### Prompt cache + tools (Anthropic)
```python
resp = client.messages.create(
    model="claude-opus-4-7",
    tools=tools,                       # 매 tools 의 cacheable
    system=[{"type": "text", "text": SYSTEM, "cache_control": {"type": "ephemeral"}}],
    messages=messages,
)
```

### MCP server (Python)
```python
from mcp.server.fastmcp import FastMCP
mcp = FastMCP("my-tools")

@mcp.tool()
def query_orders(customer_id: str) -> list[dict]:
    """Return recent orders for a customer."""
    return db.fetch_orders(customer_id)

if __name__ == "__main__":
    mcp.run()
```

## 매 결정 기준
| 상황 | Approach |
|---|---|
| Few tools, in-app | Direct SDK tools |
| Many shared tools across apps | MCP server |
| Latency-critical | Pre-fetch / parallel tools |
| Expensive tools | Cache by input hash |
| Untrusted LLM output | Validate + sandbox tool exec |
| Long agent loop | Tool budget + checkpoint |

**기본값**: 매 5-15 well-named tools, parallel calling enabled, structured errors, prompt caching on system + tool definitions.

## 🔗 Graph
- 변형: [[Function-Calling]] · [[MCP]] · [[ReAct]]
- 응용: [[Code-Agent]]
- Adjacent: [[RAG]] · [[Structured-Output]]

## 🤖 LLM 활용
**언제**: 매 external state / capability 의 필요한 모든 LLM app — search, DB, email, code exec, API call.
**언제 X**: 매 pure text generation (summary, translate) — 매 tool 매 unnecessary.

## ❌ 안티패턴
- **Vague tool names**: `"do_thing"` — 매 LLM 매 selection 의 fail.
- **Too many tools**: 매 30+ tool 매 confuse — 매 group / route.
- **No retry on transient error**: 매 502 / timeout 매 transient — 매 retry-safe + LLM 의 hint.
- **Streaming tool output to user mid-call**: 매 tool result 매 internal — final assistant text 만 user 에.
- **No max-turn limit**: 매 infinite loop 의 risk.

## 🧪 검증 / 중복
- Verified (Anthropic tool use docs, OpenAI function calling, MCP spec 2024-11, parallel tool calling).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — modern tool use + MCP + caching patterns |