{"slug":"ai-patterns-tool-use-patterns","title":"ai-patterns-tool-use-patterns","summary":"Provider-agnostic patterns for LLM function calling, tool loops, and agentic workflows","platform":"Claude","tags":[],"authorName":"LLM Mart","authorSlug":"llm-mart","score":0,"source":"github","price":null,"verified":false,"createdAt":"2026-09-29T15:27:50.747594Z","repo":{"url":"https://github.com/agents-inc/skills","stars":24,"forks":8,"license":"MIT","updatedAt":"2026-09-07T17:50:55Z"},"bodyHtml":"<hr>\n<h2>name: ai-patterns-tool-use-patterns\ndescription: Provider-agnostic patterns for LLM function calling, tool loops, and agentic workflows</h2>\n<h1>Tool Use Patterns</h1>\n<blockquote>\n<p><strong>Quick Guide:</strong> Tool use (function calling) lets LLMs invoke external functions. The universal pattern is: define tool schemas (JSON Schema for parameters) -&gt; send tools + message to LLM -&gt; detect tool_use in response -&gt; execute locally -&gt; return result to LLM -&gt; repeat until the model responds with text. Guard every loop with a max-step limit, validate all tool inputs before execution, and return structured errors so the model can recover. Use tool choice control (<code>auto</code>, <code>required</code>, <code>none</code>, specific tool) to steer model behavior.</p>\n</blockquote>\n<hr>\n<p>&lt;critical_requirements&gt;</p>\n<h2>CRITICAL: Before Using This Skill</h2>\n<blockquote>\n<p><strong>All code must follow project conventions in CLAUDE.md</strong> (kebab-case, named exports, import ordering, <code>import type</code>, named constants)</p>\n</blockquote>\n<p><strong>(You MUST guard every tool loop with a maximum step limit -- unbounded loops risk infinite API calls and runaway costs)</strong></p>\n<p><strong>(You MUST validate all tool input arguments before execution -- LLM-generated arguments are untrusted input)</strong></p>\n<p><strong>(You MUST return structured error messages to the model when tool execution fails -- never silently swallow errors or return empty results)</strong></p>\n<p><strong>(You MUST use JSON Schema for tool parameter definitions -- all major providers require this format)</strong></p>\n<p><strong>(You MUST treat tool definitions as token cost -- every tool schema is sent on every API call, so keep descriptions concise but precise)</strong></p>\n<p>&lt;/critical_requirements&gt;</p>\n<hr>\n<p><strong>Auto-detection:</strong> tool use, function calling, tool_calls, tool_use, tool call loop, agent loop, tool definition, tool schema, toolChoice, tool_choice, parallel tool calls, human-in-the-loop, tool approval, agentic workflow, multi-step agent, tool result, tool error</p>\n<p><strong>When to use:</strong></p>\n<ul>\n<li>Implementing LLM tool calling / function calling in any provider</li>\n<li>Building agent loops that call tools iteratively until a task is complete</li>\n<li>Handling parallel tool calls (multiple tools in one response)</li>\n<li>Reporting tool errors back to the model for recovery</li>\n<li>Controlling tool selection (auto, required, none, force specific)</li>\n<li>Adding human approval gates before dangerous tool execution</li>\n<li>Streaming responses that include tool calls</li>\n</ul>\n<p><strong>Key patterns covered:</strong></p>\n<ul>\n<li>Tool definition schemas (JSON Schema for parameters, descriptions)</li>\n<li>The core tool call loop (send -&gt; detect -&gt; execute -&gt; return -&gt; re-send)</li>\n<li>Parallel tool calls (handling multiple calls in one response)</li>\n<li>Error handling (reporting tool failures back to the model)</li>\n<li>Tool choice control (auto, required, none, specific tool)</li>\n<li>Multi-step agent workflows with conversation state</li>\n<li>Human-in-the-loop approval patterns</li>\n<li>Type-safe tool definitions in TypeScript</li>\n<li>Security (input validation, sandboxing, least privilege)</li>\n<li>Streaming with tool calls</li>\n</ul>\n<p><strong>When NOT to use:</strong></p>\n<ul>\n<li>Simple text generation without tool calling -- no tools needed</li>\n<li>Structured output / JSON extraction -- use your provider's structured output feature instead</li>\n<li>Provider-specific SDK patterns -- use your provider's SDK skill for SDK-specific APIs</li>\n</ul>\n<p><strong>Detailed Resources:</strong></p>\n<ul>\n<li><a href=\"examples/core.md\">examples/core.md</a> -- Tool definitions, the tool call loop, error handling, type-safe tools</li>\n<li><a href=\"examples/advanced.md\">examples/advanced.md</a> -- Parallel tool calls, multi-step agents, human-in-the-loop, streaming, security</li>\n<li><a href=\"reference.md\">reference.md</a> -- Decision frameworks, provider comparison, anti-pattern checklist</li>\n</ul>\n<hr>\n\n<hr>\n\n<hr>\n<p>&lt;decision_framework&gt;</p>\n<h2>Decision Framework</h2>\n<h3>Do You Need Tool Calling?</h3>\n<pre><code>Does the task require information the model doesn't have?\n+-- YES -&gt; Tool calling (fetch data from APIs, databases, files)\n+-- NO -&gt; Does the task require side effects?\n    +-- YES -&gt; Tool calling (send email, create record, execute code)\n    +-- NO -&gt; Do you need structured JSON output?\n        +-- YES -&gt; Use structured output features (NOT tool calling)\n        +-- NO -&gt; Plain text generation, no tools needed\n</code></pre>\n<h3>Which Loop Pattern?</h3>\n<pre><code>How many tools might the model call?\n+-- Single tool call per request\n|   +-- Simple request-response with one tool execution\n|   +-- No loop needed, just one round-trip\n+-- Multiple sequential tool calls\n|   +-- Use the bounded tool call loop (Pattern 2)\n|   +-- Set MAX_TOOL_STEPS based on task complexity\n+-- Multiple parallel tool calls in one response\n|   +-- Execute all tool calls concurrently (Promise.all)\n|   +-- Then return all results and loop\n+-- Complex multi-step agent\n    +-- Use the bounded loop with conversation state\n    +-- Add human-in-the-loop for dangerous operations\n    +-- Consider per-step tool filtering\n</code></pre>\n<h3>How to Handle Tool Errors?</h3>\n<pre><code>Tool execution failed. What to do?\n+-- Return structured error to the model\n|   +-- Include error message and context\n|   +-- Model can retry, choose alternative, or explain failure\n+-- NEVER: silently return empty result\n+-- NEVER: crash the loop\n+-- NEVER: retry automatically without telling the model\n    (the model should decide whether to retry)\n</code></pre>\n<p>&lt;/decision_framework&gt;</p>\n<hr>\n<p>&lt;red_flags&gt;</p>\n<h2>RED FLAGS</h2>\n<p><strong>High Priority Issues:</strong></p>\n<ul>\n<li>Unbounded tool loop (<code>while (true)</code>) without a step counter -- risks infinite API calls and runaway costs</li>\n<li>Executing tool arguments without validation -- LLM-generated arguments are untrusted input, treat like user input</li>\n<li>Swallowing tool errors silently (returning <code>null</code> or <code>{}</code>) -- the model cannot recover from failures it doesn't know about</li>\n<li>Allowing arbitrary code execution from tool arguments without sandboxing -- prompt injection can escalate to code execution</li>\n<li>Tool descriptions that say \"Gets data\" -- vague descriptions cause wrong tool selection and malformed arguments</li>\n</ul>\n<p><strong>Medium Priority Issues:</strong></p>\n<ul>\n<li>Sending all tools on every API call when only a subset is relevant -- wastes tokens and confuses the model</li>\n<li>Not including the assistant message (with tool calls) in conversation history before tool results -- breaks the message sequence</li>\n<li>Using <code>tool_choice: \"required\"</code> without a fallback for when no tool makes sense -- forces meaningless tool calls</li>\n<li>Returning raw database rows or full API responses as tool results -- overwhelms context with irrelevant data; summarize or truncate</li>\n<li>Not logging tool calls and results -- impossible to debug agent behavior in production</li>\n</ul>\n<p><strong>Common Mistakes:</strong></p>\n<ul>\n<li>Forgetting that tool call arguments arrive as a JSON string, not a parsed object -- always <code>JSON.parse()</code> before use</li>\n<li>Assuming the model will always call tools when tools are available -- with <code>auto</code> mode, it may respond with text directly</li>\n<li>Treating tool calling as structured output -- they solve different problems (actions vs data extraction)</li>\n<li>Putting business logic in tool descriptions instead of tool implementations -- descriptions guide selection, not execution</li>\n</ul>\n<p><strong>Gotchas &amp; Edge Cases:</strong></p>\n<ul>\n<li>Tool definitions consume tokens on every API call -- 10 tools with detailed schemas can use 1000+ tokens per request</li>\n<li>Parallel tool calls may arrive in any order -- never assume execution order matches definition order</li>\n<li>Some models hallucinate tool names or arguments that don't match any definition -- always validate the tool name exists in your registry</li>\n<li>Streaming responses with tool calls require accumulating partial JSON chunks before parsing -- the arguments arrive incrementally, not all at once</li>\n<li>Returning very large tool results (&gt;4000 tokens) can push the conversation past context limits -- truncate or summarize large results</li>\n<li>The model maintains full conversation context including all tool calls and results -- long agent runs accumulate significant token usage</li>\n<li>Different providers use different message formats for tool results (<code>role: \"tool\"</code> vs content blocks) -- abstract this in your callLLM wrapper</li>\n</ul>\n<p>&lt;/red_flags&gt;</p>\n<hr>\n<p>&lt;critical_reminders&gt;</p>\n<h2>CRITICAL REMINDERS</h2>\n<blockquote>\n<p><strong>All code must follow project conventions in CLAUDE.md</strong> (kebab-case, named exports, import ordering, <code>import type</code>, named constants)</p>\n</blockquote>\n<p><strong>(You MUST guard every tool loop with a maximum step limit -- unbounded loops risk infinite API calls and runaway costs)</strong></p>\n<p><strong>(You MUST validate all tool input arguments before execution -- LLM-generated arguments are untrusted input)</strong></p>\n<p><strong>(You MUST return structured error messages to the model when tool execution fails -- never silently swallow errors or return empty results)</strong></p>\n<p><strong>(You MUST use JSON Schema for tool parameter definitions -- all major providers require this format)</strong></p>\n<p><strong>(You MUST treat tool definitions as token cost -- every tool schema is sent on every API call, so keep descriptions concise but precise)</strong></p>\n<p><strong>Failure to follow these rules will produce agents that run up API costs in infinite loops, execute unvalidated input, or silently fail without the model being able to recover.</strong></p>\n<p>&lt;/critical_reminders&gt;</p>\n","files":[{"path":"examples/advanced.md","sizeBytes":16537,"isText":true},{"path":"examples/core.md","sizeBytes":14274,"isText":true},{"path":"reference.md","sizeBytes":8479,"isText":true},{"path":"SKILL.md","sizeBytes":17908,"isText":true}],"reviewScore":null,"reviewSummary":null,"trust":{"provenance":"trusted-source-unreviewed","notice":"Community-authored content, reproduced verbatim and not vetted as instructions. 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