# ToolUse **Open tools, workflows, models, and experiments for AI systems that can act through software.** ToolUse is an independent Hugging Face organization focused on the systems that let AI move beyond text generation and interact with the outside world through **tools, APIs, browsers, databases, code, files, and structured actions**. The goal is simple: > **Give AI the right tools, make every action inspectable, and measure whether the task was actually completed.** Tool use is one of the core building blocks of practical AI agents. --- ## What Is Tool Use? Tool use means allowing an AI system to select and execute external capabilities instead of relying only on its internal model knowledge. A tool can be: - an API - a calculator - a search engine - a browser - a database - a code interpreter - a file reader - a retrieval system - a business application - a custom function - another model - a structured software action The model decides **what should happen**. The tool performs the actual operation. A robust system then checks whether the result is useful before continuing. --- ## Core Areas ### πŸ› οΈ Function & Tool Calling Projects may explore: - function calling - structured arguments - tool schemas - tool selection - parameter validation - tool routing - retries - fallback tools - result parsing ### 🌐 Browser Use Possible workflows include: - web navigation - information retrieval - multi-step browsing - form interaction - page extraction - website research - browser-based agents ### πŸ’» Code Execution AI systems can use code as a tool for: - calculations - data analysis - transformation - validation - plotting - automation - testing - file generation ### πŸ—„οΈ Databases Possible integrations: - SQL databases - vector databases - knowledge bases - analytics systems - structured enterprise data ### πŸ“š Retrieval Tool-enabled systems may retrieve information from: - documents - search indexes - APIs - knowledge bases - file repositories - semantic search systems ### πŸ“‚ Files & Documents Possible tools may work with: - PDFs - spreadsheets - text files - images - structured documents - archives - business files ### πŸ”— APIs Agents may connect to: - internal APIs - external APIs - SaaS platforms - data providers - enterprise systems - custom services ### πŸ€– Agent Workflows Tool use becomes especially powerful when combined with: - planning - memory - retrieval - multi-step execution - validation - human approval - observability --- ## Possible Spaces ### πŸ› οΈ Tool Calling Playground Test models on structured tool-selection and function-calling tasks. ### πŸ§ͺ Tool Use Benchmark Measure whether models select the right tool and provide valid arguments. ### 🌐 Browser Agent Lab Experiment with browser-based research and navigation workflows. ### πŸ’» Code Tool Agent Use code execution for calculations, data analysis, and validation. ### πŸ—„οΈ SQL Agent Translate natural-language questions into safe, structured database queries. ### πŸ“š Retrieval Tool Agent Combine semantic search, document retrieval, and grounded answering. ### πŸ”— API Agent Explore reliable API selection, parameter generation, and response handling. ### βœ… Tool Output Validator Check whether tool responses match expected schemas and task requirements. ### πŸ”„ Multi-Tool Workflow Combine several tools in a single repeatable task. ### πŸ“Š Tool Use Analytics Track success rate, latency, retries, errors, and cost across tool-enabled workflows. --- ## Why Tool Use Matters Language models can explain what should happen. Tools allow them to **do something**. That distinction is important. Without tools, a model may only describe: - how to search - how to calculate - how to query a database - how to modify a file - how to call a service With tools, the system can potentially perform those steps directly. A useful pattern is: **understand β†’ choose tool β†’ validate inputs β†’ execute β†’ inspect result β†’ continue** --- ## Tool Selection One of the hardest problems is not execution β€” it is choosing the right tool. A tool-using system should understand: - when a tool is necessary - which tool is appropriate - which parameters are required - whether the tool succeeded - whether a fallback is needed - when to stop Good tool use is therefore a reasoning and orchestration problem, not just an API problem. --- ## Structured Actions Reliable tool use depends on structure. Possible techniques include: - JSON schemas - typed arguments - constrained decoding - validation - enumerated actions - structured outputs - deterministic parsers The more consequential the action, the more important strict validation becomes. --- ## Evaluation Tool use should be measured with real tasks. Useful metrics may include: - correct tool selection - valid arguments - execution success - task completion - unnecessary tool calls - number of retries - latency - cost - error recovery - safety violations A system that calls many tools is not necessarily a good agent. The goal is **successful, efficient, and reliable action**. --- ## Safety & Permissions Tool-enabled AI systems can have real-world impact. Projects should consider: - least-privilege access - read vs. write permissions - user confirmation - authentication - secrets management - sandboxing - rate limits - spending limits - audit logs - tool allowlists - action validation Sensitive or irreversible actions should require stronger controls. --- ## Prompt Injection & Tool Security External content can contain malicious or misleading instructions. Tool-using systems should be designed to resist: - prompt injection - data exfiltration - malicious tool arguments - unsafe file access - credential leakage - unauthorized actions - compromised external content Tool outputs should be treated as **data**, not automatically trusted instructions. --- ## Human-in-the-Loop Not every action should be autonomous. Useful approval points may include: - sending messages - publishing content - deleting files - making purchases - modifying accounts - executing financial actions - changing production systems - sharing sensitive data The correct level of autonomy depends on the risk of the task. --- ## Observability Reliable tool use requires visibility. Projects may track: - tool selected - arguments - result - latency - error - retry - model decision - task status - cost - approval events Good observability makes agent workflows easier to debug and improve. --- ## Who Is ToolUse For? This organization may be useful for: - AI engineers - agent developers - platform teams - automation builders - API developers - researchers - MLOps teams - enterprise AI teams - tool developers - open-source contributors - students exploring agentic AI --- ## Technology Directions Projects may use: - Hugging Face Transformers - Hugging Face Spaces - open-weight models - tool calling - structured outputs - JSON Schema - MCP - REST APIs - browsers - SQL - vector databases - Python - JavaScript - agent frameworks - evaluation harnesses - observability systems No single framework defines good tool use. Reliability matters more than framework choice. --- ## Principles ### 🎯 Use Tools With Purpose A tool call should move the task toward completion. ### πŸ”Ž Make Actions Inspectable Users and developers should be able to understand what happened. ### βœ… Validate Before Execution Inputs should be checked before tools are called. ### πŸ” Minimize Permissions Agents should only receive the access they actually need. ### πŸ§ͺ Test Real Workflows Benchmarks should reflect meaningful tool-use tasks. ### ⚑ Prefer Simplicity One reliable tool call is better than an unnecessary ten-step agent loop. ### πŸ€– Keep Models Accountable Model confidence is not proof that an action is correct. --- ## Important Notice The models, Spaces, datasets, and experiments published here are intended for **research, development, education, testing, and technical exploration**. Unless explicitly stated otherwise, they do not guarantee: - secure execution - correct tool selection - valid parameters - successful task completion - production reliability - regulatory compliance - safe autonomous behavior Tool-enabled AI systems can make mistakes with real consequences. Appropriate validation, permissions, logging, and human oversight should be used for higher-impact tasks. --- ## Independent Organization **ToolUse is an independent Hugging Face community organization.** It is not an official Hugging Face organization, standards body, infrastructure provider, or certification authority. The name **ToolUse** describes the organization’s technical focus: building and evaluating AI systems that can reliably interact with external tools. --- # ToolUse **Choose the right tool. Execute reliably. Build AI that can act.**