'tool calling' 검색 결과

Design and validate model-facing tool definitions with clear names, action-oriented descriptions, bounded JSON Schema parameters, explicit side effects, safe defaults, idempotency, errors, and realistic tests. Use when creating function-calling tools, MCP tools, agent actions, structured tool inputs, or when a model selects the wrong tool, invents arguments, or causes unsafe side effects.

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Expert en construction d'outils pour agents IA (function calling, tool schemas, MCP, validation, error handling)

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AI 에이전트와 외부 도구를 잇는 MCP 서버를 명령으로 관리합니다. 서버 목록, 도구 입출력 규격, 연결 설정과 인증을 확인하고 도구를 호출합니다.

• MCP 도구와 인수 규격을 확인합니다
• MCP 연결을 설정하고 인증합니다
• JSON 인수로 도구를 호출합니다

AI 에이전트의 도구 구성, 입력과 출력 형식, 오류 복구 절차를 설계합니다. 문맥 정보량을 관리하고 완료율, 재시도 횟수, 성공한 작업당 비용으로 개선을 평가합니다.

• 에이전트 도구와 입력 형식을 설계합니다
• 도구 결과와 다음 행동을 명확히 합니다
• 안전한 재시도와 중단 조건을 정합니다

터미널에서 MCP 서버와 도구를 찾고 인증, 호출, 설정 관리를 수행합니다. 임시 연결, 연결 유지 프로세스 관리, CLI와 TypeScript 타입 및 클라이언트 생성도 지원합니다.

• 설정된 MCP 서버와 도구 확인
• MCP 도구를 터미널에서 한 번 호출
• 서버 인증과 설정 관리

Guides building chatbots and tool-using agents with LangChain. Covers conversation history, model provider integration, and RAG, which retrieves relevant documents to inform answers, plus tracing to inspect application behavior.

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• Build a chatbot that remembers conversation history
• Create document question answering with source references
• Build an agent that uses custom tools

Answer questions about the AI SDK and help build AI-powered features. Use when developers: (1) Ask about AI SDK functions like generateText, streamText, ToolLoopAgent, embed, or tools, (2) Want to build AI agents, chatbots, RAG systems, or text generation features, (3) Have questions about AI providers (OpenAI, Anthropic, Google, etc.), streaming, tool calling, structured output, or embeddings, (4) Use React hooks like useChat or useCompletion. Triggers on: "AI SDK", "Vercel AI SDK", "generateText", "streamText", "add AI to my app", "build an agent", "tool calling", "structured output", "useChat".

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Provides Spring Boot MCP server patterns that create Model Context Protocol servers with Spring AI by defining tool handlers, exposing resources, configuring prompt templates, and setting up transports for AI function calling and tool calling. Use when building MCP servers to extend AI capabilities with Spring's official AI framework, implementing AI tools, custom function calling, or MCP client integration.

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Provides LangChain4j patterns for implementing MCP (Model Context Protocol) servers, creating Java AI tools, exposing tool calling capabilities, and integrating MCP clients with AI services. Use when building a Java MCP server, implementing tool calling in Java, connecting LangChain4j to external MCP servers, or securing tool exposure for agent workflows.

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Answer questions about the AI SDK and help build AI-powered features. Use when developers: (1) Ask about AI SDK functions like generateText, streamText, ToolLoopAgent, embed, or tools, (2) Want to build AI agents, chatbots, RAG systems, or text generation features, (3) Have questions about AI providers (OpenAI, Anthropic, Google, etc.), streaming, tool calling, structured output, or embeddings, (4) Use React hooks like useChat or useCompletion. Triggers on: "AI SDK", "Vercel AI SDK", "generateText", "streamText", "add AI to my app", "build an agent", "tool calling", "structured output", "useChat".

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Developer reference for building applications, tools, and agents on the Pi Durable agent harness (@earendil-works/pi-durable) and the pi-ai model layer. Use when writing or debugging code that imports @earendil-works/pi-durable or pi-ai: assembling a Harness over memory/SQLite/JSONL/Durable Object storage, submitting input and waiting for answers, defining tools with TypeBox schemas and structured output, calling tools from tools, hooks and guards, durable tasks with checkpoints and ownership, subagents and child tasks, documents and migrations, code mode, tool search, per-conversation environments, compaction, recovery after a crash, storage and environment conformance tests, or choosing models and providers. Also use when a durable/agent-runtime behavior question comes up: what survives a restart, why an input stayed queued, why a task is blocked or orphaned, how abort cascades, or what the Pico5 specification requires.

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Designs robust function/tool calling schemas for LLMs with JSON schemas, validation strategies, typed interfaces, and example calls. Use when implementing "function calling", "tool use", "LLM tools", or "agent actions".

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Design tools that AI agents can choose and use correctly. Define clear descriptions, inputs, and outputs, manage large search results, and plan approval checks, isolated execution environments, and interfaces for collaboration between agents.

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• Reviewing the scope of tools for an MCP server
• Improving tool descriptions and input examples
• Designing pagination and truncation for search results

Design autonomous coding agents with patterns inspired by Cline and OpenAI Codex. Explore tool calling interfaces, permission and approval systems, browser automation, and workflows where people review or guide the agent's actions.

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• Planning an autonomous coding agent
• Designing tool and function calling interfaces
• Defining permissions and approval steps

Design AI agents that use tools, plan tasks, retain relevant context, and recover from failures. Choose execution patterns, permission boundaries, memory handling, and runtime limits, and assess whether coordinating multiple agents is justified for the task.

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• Choosing an execution loop for a tool-using agent
• Designing memory and recovery for long-running tasks
• Setting tool permissions, runtime limits, and cost caps

Design autonomous coding agents using implementation patterns for tool calls, permission checks, browser interaction, and saved task state. Code examples and checklists help structure agent behavior and workflows that include human approval.

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• Designing a coding agent's execution loop
• Defining tool interfaces for files and commands
• Adding permissions and human approval steps

Detects focal chromatin loops (point interactions / corner-dots) in balanced Hi-C and Micro-C contact maps and aggregates/validates a loop set. Covers de-novo calling with cooltools dots (HiCCUPS-style 4-background local enrichment with lambda-chunked FDR), chromosight (template-correlation), and Mustache (scale-space blob detection); aggregate peak analysis (APA) via cooltools pileup for confirmation; the depth/resolution prerequisite (de-novo needs ~5-10kb resolution = hundreds of millions to billions of valid pairs); consensus across callers and convergent-CTCF support as validation; and differential loops via union anchors plus chromosight quantify. Use when calling chromatin loops or dots from a cooler, deciding whether a map is deep enough to call de-novo vs running APA on known CTCF/cohesin anchors, building an aggregate peak pileup, comparing loops across conditions, or validating loop calls. For HiChIP/PLAC-seq/PCHi-C protein-anchored data use FitHiChIP/MAPS, not dots.

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CLI for VCF/BCF: filter, merge, annotate, query, normalize, compute stats. Core post-variant-calling: quality filtering, multi-sample merging, rsID annotation, genotype extraction. Samtools companion in HTSlib. Use GATK for complex indel realignment during calling; use VCFtools for population genetics stats.

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Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration. Use when "build agent, AI agent, autonomous agent, tool use, function calling, multi-agent, agent memory, agent planning, langchain agent, crewai, autogen, claude agent sdk, ai-agents, langchain, autogen, crewai, tool-use, function-calling, autonomous, llm, orchestration" mentioned.

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Comprehensive patterns for building AI-powered code generation tools, code assistants, automated refactoring, code review, and structured output generation using LLMs with function calling and tool use. Use when "code generation, AI code assistant, function calling, structured output, code review AI, automated refactoring, tool use, code completion, agent code, " mentioned.

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Expert guide for parallel tool execution, MCP batching (vibes_batch), asynchronous Promise.allSettled pipelines, subagent concurrent fan-out, and latency-optimized tool calling loops / Panduan ahli eksekusi tool paralel dan batching MCP.

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Add a new tool to the backend OpenAI function calling system. Use when user mentions "new tool", "add tool", "backend function", "agent capability", or wants to extend what the AI agent can do.

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Build AI agents with Hugging Face's SmolAgents framework. Use when creating code-executing agents, tool-calling agents, multi-agent systems, agentic RAG, text-to-SQL pipelines, web browsing agents, or any multi-step AI workflows. Covers CodeAgent, ToolCallingAgent, custom tools, MCP integration, memory management, secure code execution (E2B, Docker, Blaxel), and model configuration (HF Inference, LiteLLM, Transformers, Ollama).

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Integrate on-device AI using Foundation Models framework, Core ML, and open-source LLM runtimes on Apple Silicon. Covers Foundation Models (LanguageModelSession, @Generable, @Guide, SystemLanguageModel, structured output, tool calling), Core ML (coremltools, model conversion, quantization, palettization, pruning, Neural Engine, MLTensor), MLX Swift (transformer inference, unified memory), and llama.cpp (GGUF, cross-platform LLM). Use when building tool-calling AI features, working with guided generation schemas, converting models, or running on-device inference.

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