# Tool Use

> YogoQ Core AI-readable term handoff. Preview, read-only, Reviewed/Verified only.

- Canonical URL: https://core.yogoq.com/en-US/core/tool-use
- Locale: en-US
- Content tier: db_backed
- Quality: reviewed
- Publication status: published_reviewed
- Schema version: core-reviewed-term-ai-handoff-v2
- Compatible with: core-reviewed-term-ai-handoff-v1
- Content hash: 2f4d5b65420ba825a099a02c19695b56e999e835d194164d235f46cf2d6a7ffb
- Trust policy: core-trust-policy-v1-2026-06-22

## Short Definition

Tool use lets an AI model call external capabilities such as search, databases, calculators, APIs, or workflow actions. It expands capability, but permissions and confirmation become central design issues.

## 一言でいうと

Tool use lets an AI model call external capabilities such as search, databases, calculators, APIs, or workflow actions. It expands capability, but permissions and confirmation become central design issues.

## 計算の考え方

Evaluate tool use by tool-selection accuracy, call success, and action safety. Selection accuracy | Correct tool choices / tool-choice decisions | Measures model routing quality Execution success rate | Successful calls / calls | Measures schema and integration reliability Approval compliance | Approved high-impact calls / high-impact calls | Checks safe operation

- Selection accuracy | Correct tool choices / tool-choice decisions | Measures model routing quality
- Execution success rate | Successful calls / calls | Measures schema and integration reliability
- Approval compliance | Approved high-impact calls / high-impact calls | Checks safe operation

## 含めるもの / 含めないもの

Tool use lets AI call external capabilities; it does not automatically provide business approval or safe authorization. Include | Search, calculation, database lookup, API calls, file reading, draft creation | External capability use Exclude | Unlimited permissions, ownerless sending, unapproved deletion or purchase | Needs safety design Make explicit | Tool description, input schema, permissions, confirmation, logs | Reduces misoperation

- Include | Search, calculation, database lookup, API calls, file reading, draft creation | External capability use
- Exclude | Unlimited permissions, ownerless sending, unapproved deletion or purchase | Needs safety design
- Make explicit | Tool description, input schema, permissions, confirmation, logs | Reduces misoperation

## 意味

Tool use is the mechanism by which an AI model invokes external systems or functions to retrieve information, compute results, or take actions the model cannot perform by itself. Examples include search, file retrieval, database lookup, calculators, ticket creation, email drafting, and MCP tools. In AI agents, tool use is a core part of execution. Production design needs tool names, descriptions, input schemas, returned fields, permissions, confirmation requirements, error handling, and logs to reduce wrong tool selection and unsafe operations.

## 役立つ場面

Teams can decide when the model should answer directly versus retrieve or compute through a tool. Agent toolsets can be classified into read, draft, execute, and send categories. High-impact tools can require human confirmation while low-risk tools remain fast.

- Teams can decide when the model should answer directly versus retrieve or compute through a tool.
- Agent toolsets can be classified into read, draft, execute, and send categories.
- High-impact tools can require human confirmation while low-risk tools remain fast.

## 使い方のポイント

- Tool use is the AI's ability to call external capabilities.
- AI agent execution quality depends heavily on tool design.
- Descriptions, schemas, returned fields, permissions, and logs matter.
- High-impact actions need confirmation and rollback.
- MCP is one way to standardize tool and resource exposure.

## 何が数字を動かすか

Quality depends on tool granularity, descriptions, schemas, permissions, and error design. Granularity | Narrow tools are safer but too many tools complicate choice Description | The model needs clear when-to-use guidance Schema | Input constraints reduce execution failures Permissions | Separating read and write rights limits impact

- Granularity | Narrow tools are safer but too many tools complicate choice
- Description | The model needs clear when-to-use guidance
- Schema | Input constraints reduce execution failures
- Permissions | Separating read and write rights limits impact

## 判断するときの注意点

More tools can increase wrong-tool and permission risk. Expose tools with least privilege, and separate write or external-send actions. Define stop conditions so the model does not improvise dangerous alternatives after errors. Do not execute high-impact actions without showing the user the evidence and action.

- Expose tools with least privilege, and separate write or external-send actions.
- Define stop conditions so the model does not improvise dangerous alternatives after errors.
- Do not execute high-impact actions without showing the user the evidence and action.

## よくある誤解 / 落とし穴

- More tools do not automatically make a system smarter. They can make selection and safety harder.
- Read and write tools should not be treated the same. Their impact differs.
- Tool calls should not be fully delegated to the model for high-impact operations.

## 最小例

An internal AI answers billing questions by using an invoice database lookup tool. The first version is read-only; it cannot resend invoices or edit amounts. The tool accepts customer ID and billing month, and returns only invoice number, amount, and payment state. The pilot works, but some users ask by customer name and the tool fails, so the team improves input guidance and error handling. Later, an invoice-resend draft tool is added, but sending still requires human confirmation.

## 似ている言葉との違い

Tool Use | Model calls external capabilities | Extends AI capability API | System interface | Often sits behind a tool RPA | Executes predefined steps | Can run without AI reasoning

- Tool Use | Model calls external capabilities | Extends AI capability
- API | System interface | Often sits behind a tool
- RPA | Executes predefined steps | Can run without AI reasoning

## 一緒に見る指標

Tool use should be read with AI agents, MCP, and prompt injection. AI Agent | Uses tools to do work | Defines execution scope MCP | Standardizes tool exposure | Reduces custom integration work Prompt Injection | Untrusted input can redirect tool use | Requires safety design

- AI Agent | Uses tools to do work | Defines execution scope
- MCP | Standardizes tool exposure | Reduces custom integration work
- Prompt Injection | Untrusted input can redirect tool use | Requires safety design

## Aliases

- Tool Use (display_name, en-US)
- ツール呼び出し (katakana, en-US)
- Tool Use (english_name, en-US)
- ツール利用 (localized_title, ja-JP)

## Relations

- AI Agent: related (https://core.yogoq.com/en-US/core/ai-agent)
- Model Context Protocol: related (https://core.yogoq.com/en-US/core/model-context-protocol)
- API: compare (https://core.yogoq.com/en-US/core/api)

## RAG Chunks

- core:chunk:tool-use:en-US:definition:e130d4d3bd809a09
- core:chunk:tool-use:en-US:formula:51567f2e18ed1021
- core:chunk:tool-use:en-US:boundary:c376fc12a3ef10f3
- core:chunk:tool-use:en-US:meaning:c9e1470a9b6f3662
- core:chunk:tool-use:en-US:usage:304fe2d2ad53f980
- core:chunk:tool-use:en-US:usage:334715e81e5d8275
- core:chunk:tool-use:en-US:drivers:bbcf6b8799e0728c
- core:chunk:tool-use:en-US:misunderstandings:1f381a245f808f3d
- core:chunk:tool-use:en-US:misunderstandings:6f9c3ec6b5fc548c
- core:chunk:tool-use:en-US:examples:4c1b2facda82533c
- core:chunk:tool-use:en-US:comparisons:077d51ff44db2698
- core:chunk:tool-use:en-US:related_metrics:f422f3b1dd5515c9
- core:chunk:tool-use:en-US:faq:6e2cd56e535ce650
- core:chunk:tool-use:en-US:faq:13a81b0ca098d9ec
- core:chunk:tool-use:en-US:faq:33ada4cbc858dc67

## FAQ

### Is tool use the same as an API?

No. An API is a system interface; tool use is the AI-facing design for when and how the model invokes that interface.

### Should every tool be available to the AI?

No. Start with least privilege and separate read, draft, execute, and send actions.

### How does MCP relate?

MCP is one standard way to expose tools and resources to AI applications.

## Sources

- Model Context Protocol: Tools - https://modelcontextprotocol.io/docs/concepts/tools
- Model Context Protocol: Specification - https://modelcontextprotocol.io/specification
- NIST: AI RMF - https://nvlpubs.nist.gov/nistpubs/ai/nist.ai.100-1.pdf

## Limitations

This page is reference information for research and learning. For accounting, legal, finance, health, security, or other individual decisions, confirm against primary sources or qualified professionals.

- Public pages support general understanding and practical context; they are not professional advice for individual cases.
- Fast-changing information such as regulations, accounting standards, prices, product specs, and legal requirements should be checked against primary sources before final decisions.
- Even when AI-assisted drafting or audit is used, publication relies on quality gates and human-readable evidence.