data-tool-use-concepts

name	Data: Tool use concepts
description	Conceptual foundations of tool use with the Claude API including tool definitions, tool choice, and best practices
ccVersion	2.1.157
allowed-tools	Read Write Edit Bash
license	BSD-3-Clause license
metadata	{"skill-author":"Lord1Egypt"}

Tool Use Concepts

This file covers the conceptual foundations of tool use with the Claude API. For language-specific code examples, see the python/, typescript/, or other language folders. For decision heuristics on which tools to expose, how to manage context in long-running agents, and caching strategy, see agent-design.md.

User-Defined Tools

Tool Definition Structure

Note: When using the Tool Runner (beta), tool schemas are generated automatically from your function signatures (Python), Zod schemas (TypeScript), annotated classes (Java), jsonschema struct tags (Go), or BaseTool subclasses (Ruby). The raw JSON schema format below is for the manual approach — including PHP's BetaRunnableTool, which wraps a run closure around a hand-written schema — or SDKs without tool runner support.

Each tool requires a name, description, and JSON Schema for its inputs:

{
  "name": "get_weather",
  "description": "Get current weather for a location",
  "input_schema": {
    "type": "object",
    "properties": {
      "location": {
        "type": "string",
        "description": "City and state, e.g., San Francisco, CA"
      },
      "unit": {
        "type": "string",
        "enum": ["celsius", "fahrenheit"],
        "description": "Temperature unit"
      }
    },
    "required": ["location"
  

Best practices for tool definitions:

• Use clear, descriptive names (e.g., get_weather, search_database, send_email)
• Write detailed descriptions — Claude uses these to decide when to use the tool. Be prescriptive about when to call it, not just what it does (e.g. "Call this when the user asks about current prices or recent events"). On recent Opus models, which reach for tools more conservatively, trigger conditions in the description give measurable lift in should-call rate.
• Include descriptions for each property
• Use enum for parameters with a fixed set of values
• Mark truly required parameters in required; make others optional with defaults

---

Tool Choice Options

Control when Claude uses tools:

Value	Behavior
{"type": "auto"}	Claude decides whether to use tools (default)
{"type": "any"}	Claude must use at least one tool
{"type": "tool", "name": "..."}	Claude must use the specified tool
{"type": "none"}	Claude cannot use tools

Any tool_choice value can also include "disable_parallel_tool_use": true to force Claude to use at most one tool per response. By default, Claude may request multiple tool calls in a single response.

---

Tool Runner vs Manual Loop

Tool Runner (Recommended): The SDK's tool runner handles the agentic loop automatically — it calls the API, detects tool use requests, executes your tool functions, feeds results back to Claude, and repeats until Claude stops calling tools. Available in Python, TypeScript, Java, Go, Ruby, and PHP SDKs (beta). The Python SDK also provides MCP conversion helpers (anthropic.lib.tools.mcp) to convert MCP tools, prompts, and resources for use with the tool runner — see python/claude-api/tool-use.md for details.

Manual Agentic Loop: Use when you need fine-grained control over the loop (e.g., custom logging, conditional tool execution, human-in-the-loop approval). Loop until stop_reason == "end_turn", always append the full response.content to preserve tool_use blocks, and ensure each tool_result includes the matching tool_use_id.

Stop reasons for server-side tools: When using server-side tools (code execution, web search, etc.), the API runs a server-side sampling loop. If this loop reaches its default limit of 10 iterations, the response will have stop_reason: "pause_turn". To continue, re-send the user message and assistant response and make another API request — the server will resume where it left off. Do NOT add an extra user message like "Continue." — the API detects the trailing server_tool_use block and knows to resume automatically.

# Handle pause_turn in your agentic loop
if response.stop_reason == "pause_turn":
    messages = [
        {"role": "user", "content": user_query},
        {"role": "assistant", "content": response.content},
    ]
    # Make another API request — server resumes automatically
    response = client.messages.create(
        model="{{OPUS_ID}}", messages=messages, tools=tools
    )

Set a max_continuations limit (e.g., 5) to prevent infinite loops. For the full guide, see: https://platform.claude.com/docs/en/build-with-claude/handling-stop-reasons

Security: The tool runner executes your tool functions automatically whenever Claude requests them. For tools with side effects (sending emails, modifying databases, financial transactions), validate inputs within your tool functions and consider requiring confirmation for destructive operations. Use the manual agentic loop if you need human-in-the-loop approval before each tool execution.

---

Handling Tool Results

When Claude uses a tool, the response contains a tool_use block. You must:

1. Execute the tool with the provided input
2. Send the result back in a tool_result message
3. Continue the conversation

Error handling in tool results: When a tool execution fails, set "is_error": true and provide an informative error message. Claude will typically acknowledge the error and either try a different approach or ask for clarification.

Multiple tool calls: Claude can request multiple tools in a single response. Handle them all before continuing — send all results back in a single user message.

---

Server-Side Tools: Code Execution

The code execution tool lets Claude run code in a secure, sandboxed container. Unlike user-defined tools, server-side tools run on Anthropic's infrastructure — you don't execute anything client-side. Just include the tool definition and Claude handles the rest.

Key Facts

• Runs in an isolated container (1 CPU, 5 GiB RAM, 5 GiB disk)
• No internet access (fully sandboxed)
• Python 3.11 with data science libraries pre-installed
• Containers persist for 30 days and can be reused across requests
• Free when used with web search/web fetch tools; otherwise $0.05/hour after 1,550 free hours/month per organization

Tool Definition

The tool requires no schema — just declare it in the tools array:

{
  "type": "code_execution_20260120",
  "name": "code_execution"
}

Claude automatically gains access to bash_code_execution (run shell commands) and text_editor_code_execution (create/view/edit files).

Pre-installed Python Libraries

• Data science: pandas, numpy, scipy, scikit-learn, statsmodels
• Visualization: matplotlib, seaborn
• File processing: openpyxl, xlsxwriter, pillow, pypdf, pdfplumber, python-docx, python-pptx
• Math: sympy, mpmath
• Utilities: tqdm, python-dateutil, pytz, sqlite3

Additional packages can be installed at runtime via pip install.

Supported File Types for Upload

Type	Extensions
Data	CSV, Excel (.xlsx/.xls), JSON, XML
Images	JPEG, PNG, GIF, WebP
Text	.txt, .md, .py, .js, etc.

Container Reuse

Reuse containers across requests to maintain state (files, installed packages, variables). Extract the container_id from the first response and pass it to subsequent requests.

Response Structure

The response contains interleaved text and tool result blocks:

• text — Claude's explanation
• server_tool_use — What Claude is doing
• bash_code_execution_tool_result — Code execution output (check return_code for success/failure)
• text_editor_code_execution_tool_result — File operation results

Security: Always sanitize filenames with os.path.basename() / path.basename() before writing downloaded files to disk to prevent path traversal attacks. Write files to a dedicated output directory.

---

Server-Side Tools: Web Search and Web Fetch

Web search and web fetch let Claude search the web and retrieve page content. They run server-side — just include the tool definitions and Claude handles queries, fetching, and result processing automatically.

Tool Definitions

[
  { "type": "web_search_20260209", "name": "web_search" },
  { "type": "web_fetch_20260209", "name": "web_fetch" }
]

Dynamic Filtering (Opus 4.8 / Opus 4.7 / Opus 4.6 / Sonnet 4.6)

The web_search_20260209 and web_fetch_20260209 versions support dynamic filtering — Claude writes and executes code to filter search results before they reach the context window, improving accuracy and token efficiency. Dynamic filtering is built into these tool versions and activates automatically; you do not need to separately declare the code_execution tool or pass any beta header.

{
  "tools": [
    { "type": "web_search_20260209", "name": "web_search" },
    { "type": "web_fetch_20260209", "name": "web_fetch" }
  ]
}

Without dynamic filtering, the previous web_search_20250305 version is also available.

Note: Only include the standalone code_execution tool when your application needs code execution for its own purposes (data analysis, file processing, visualization) independent of web search. Including it alongside _20260209 web tools creates a second execution environment that can confuse the model.

---

Server-Side Tools: Programmatic Tool Calling

With standard tool use, each tool call is a round trip: Claude calls, the result enters Claude's context, Claude reasons, then calls the next tool. Chained calls accumulate latency and tokens — most of that intermediate data is never needed again.

Programmatic tool calling lets Claude compose those calls into a script. The script runs in the code execution container; when it invokes a tool, the container pauses, the call executes, and the result returns to the running code (not to Claude's context). The script processes it with normal control flow. Only the final output returns to Claude. Use it when chaining many tool calls or when intermediate results are large and should be filtered before reaching the context window.

For full documentation, use WebFetch:

• URL: https://platform.claude.com/docs/en/agents-and-tools/tool-use/programmatic-tool-calling

---

Server-Side Tools: Tool Search

The tool search tool lets Claude dynamically discover tools from large libraries without loading all definitions into the context window. Use it when you have many tools but only a few are relevant to any given request. Discovered tool schemas are appended to the request, not swapped in — this preserves the prompt cache (see agent-design.md §Caching for Agents).

For full documentation, use WebFetch:

• URL: https://platform.claude.com/docs/en/agents-and-tools/tool-use/tool-search-tool

---

Skills

Skills package task-specific instructions that Claude loads only when relevant. Each skill is a folder containing a SKILL.md file. The skill's short description sits in context by default; Claude reads the full file when the current task calls for it. Use skills to keep specialized instructions out of the base system prompt without losing discoverability.

For full documentation, use WebFetch:

• URL: https://platform.claude.com/docs/en/agents-and-tools/skills

---

Tool Use Examples

You can provide sample tool calls directly in your tool definitions to demonstrate usage patterns and reduce parameter errors. This helps Claude understand how to correctly format tool inputs, especially for tools with complex schemas.

For full documentation, use WebFetch:

• URL: https://platform.claude.com/docs/en/agents-and-tools/tool-use/implement-tool-use

---

Server-Side Tools: Computer Use

Computer use lets Claude interact with a desktop environment (screenshots, mouse, keyboard). It can be Anthropic-hosted (server-side, like code execution) or self-hosted (you provide the environment and execute actions client-side).

For full documentation, use WebFetch:

• URL: https://platform.claude.com/docs/en/agents-and-tools/computer-use/overview

---

Context Editing

Context editing clears stale tool results and thinking blocks from the transcript as a long-running agent accumulates turns. Unlike compaction (which summarizes), context editing prunes — the cleared content is removed, not replaced. Use it when old tool outputs are no longer relevant and you want to keep the transcript lean without losing the conversation structure. Thresholds for what to clear are configurable.

For full documentation, use WebFetch:

• URL: https://platform.claude.com/docs/en/build-with-claude/context-editing

---

Server-Side Tools: Advisor (Beta)

The advisor tool lets Claude consult a secondary model during a conversation. The advisor runs its own API call with a model you specify and returns its analysis to the primary model. Use it when you want a second opinion, specialized expertise, or cross-model verification without managing the orchestration yourself.

Tool Definition

{
  "type": "advisor_20260301",
  "name": "advisor",
  "model": "claude-sonnet-4-6"
}

The model parameter is required — it specifies which model the advisor uses for its own inference. Optional fields: caching, max_uses, allowed_callers, defer_loading, strict.

Beta header required: advisor-tool-2026-03-01. The SDK sets this automatically when using client.beta.messages.create() with advisor tools.

---

Client-Side Tools: Memory

The memory tool enables Claude to store and retrieve information across conversations through a memory file directory. Claude can create, read, update, and delete files that persist between sessions.

Key Facts

• Client-side tool — you control storage via your implementation
• Supports commands: view, create, str_replace, insert, delete, rename
• Operates on files in a /memories directory
• The Python, TypeScript, and Java SDKs provide helper classes/functions for implementing the memory backend

Security: Never store API keys, passwords, tokens, or other secrets in memory files. Be cautious with personally identifiable information (PII) — check data privacy regulations (GDPR, CCPA) before persisting user data. The reference implementations have no built-in access control; in multi-user systems, implement per-user memory directories and authentication in your tool handlers.

For full implementation examples, use WebFetch:

• Docs: https://platform.claude.com/docs/en/agents-and-tools/tool-use/memory-tool.md

---

Structured Outputs

Structured outputs constrain Claude's responses to follow a specific JSON schema, guaranteeing valid, parseable output. This is not a separate tool — it enhances the Messages API response format and/or tool parameter validation.

Two features are available:

• JSON outputs (output_config.format): Control Claude's response format
• Strict tool use (strict: true): Guarantee valid tool parameter schemas

Supported models: {{OPUS_NAME}}, {{SONNET_NAME}}, and {{HAIKU_NAME}}. Legacy models (Claude Opus 4.5, Claude Opus 4.1) also support structured outputs.

Recommended: Use client.messages.parse() which automatically validates responses against your schema. When using messages.create() directly, use output_config: {format: {...}}. The output_format convenience parameter is also accepted by some SDK methods (e.g., .parse()), but output_config.format is the canonical API-level parameter.

JSON Schema Limitations

Supported:

• Basic types: object, array, string, integer, number, boolean, null
• enum, const, anyOf, allOf, $ref/$def
• String formats: date-time, time, date, duration, email, hostname, uri, ipv4, ipv6, uuid
• additionalProperties: false (required for all objects)

Not supported:

• Recursive schemas
• Numerical constraints (minimum, maximum, multipleOf)
• String constraints (minLength, maxLength)
• Complex array constraints
• additionalProperties set to anything other than false

The Python and TypeScript SDKs automatically handle unsupported constraints by removing them from the schema sent to the API and validating them client-side.

Important Notes

• First request latency: New schemas incur a one-time compilation cost. Subsequent requests with the same schema use a 24-hour cache.
• Refusals: If Claude refuses for safety reasons (stop_reason: "refusal"), the output may not match your schema.
• Token limits: If stop_reason: "max_tokens", output may be incomplete. Increase max_tokens.
• Incompatible with: Citations (returns 400 error), message prefilling.
• Works with: Batches API, streaming, token counting, extended thinking.

---

Tips for Effective Tool Use

1. Provide detailed descriptions: Claude relies heavily on descriptions to understand when and how to use tools
2. Use specific tool names: get_current_weather is better than weather
3. Validate inputs: Always validate tool inputs before execution
4. Handle errors gracefully: Return informative error messages so Claude can adapt
5. Limit tool count: Too many tools can confuse the model — keep the set focused
6. Test tool interactions: Verify Claude uses tools correctly in various scenarios

For detailed tool use documentation, use WebFetch:

• URL: https://platform.claude.com/docs/en/agents-and-tools/tool-use/overview