Tools – Usage Guide

extending the power of the digital human digital humans are more than chatbots—they are assistants, analysts, editors, designers but to be truly useful in real workflows, they need to do more than just talk tools extend the intelligence of the digital human into real systems they turn the digital human into a real teammate tools can also be used to connect the digital human with automation platforms like zapier , make , or n8n this allows them to trigger workflows, move data between apps, send messages, update crms, or even control physical systems—bridging the gap between natural language and real world action this guide explains how to extend the capabilities of your digital human in unith using tools —custom integrations that allow your assistant to take real actions send data, call apis, trigger workflows, or fetch internal resources in this guide, you’ll learn what tools are and how they work how to configure them via api how to define prompt logic so the llm knows when and how to use them best practices to ensure reliable behavior what is a “tool”? in unith, a tool is a special capability that a digital human can activate during a conversation to do something beyond just talking each tool acts as a bridge between natural language and specialized software capabilities when a user makes a request—explicitly or implicitly—the digital human can choose to invoke a tool, retrieve results, and integrate them into the response in real time this makes tools a key ingredient in transforming digital humans from passive conversationalists into active collaborators what does a tool actually do? a tool is, in essence, a function call to an external system , triggered by the llm based on the flow of a conversation it's like giving the digital human a button to push—when the right context appears, it pushes it for you here are some examples when a user says “can you open a support ticket for me?” , the digital human can extract the user’s name, email, and problem, then call a tool (webhook) that creates a ticket in your helpdesk system the llm interprets this logic, collects the required data throughout the conversation, and when all parameters are present, fires off a request to the tool’s endpoint tool configuration via api for now, tools are configured programmatically through the unith api support for tool management directly in interface is coming soon note no api config needed if you're using zapier if you're using zapier , there's no need to configure tools manually via the api you can use the official unith zapier app , which handles tool exposure and triggering automatically if you prefer working with other automation platforms , you can also follow our dedicated guides for make com or n8n when you configure a tool, you're telling the digital human what the tool does, what input it needs, and where to send the data this is done with a json definition, which you send to the unith api using a put request step 1 write the tool json the json describes what the tool is called (name) what it does (description) what parameters it needs from the user (e g name, email, issue) where to send the data (url – typically your api or a webhook) example tool to create a support ticket { "conversationsettings" { "tools" \[ { "name" "create ticket", "description" "opens a support ticket with the user's information ", "parameters" \[ { "name" "username", "description" "user's full name", "type" "string" }, { "name" "useremail", "description" "user's email address", "type" "string" }, { "name" "userphone", "description" "user's phone number", "type" "string" }, { "name" "userissue", "description" "description of the issue reported", "type" "string" } ], "url" "https //yourdomain com/api/ticket" } ] } } step 2 apply the tool to a digital human use the /head/{id}/conversation settings endpoint with the put method to configure one or more tools for a digital human https //platform api unith ai/head/ {head id} {head id} /conversation settings replace {head id} with the id of your digital human (available in the "basic details" section in interface) the authorization token with your bearer token the d body with your tool json example full request curl x 'put' \\ 'https //platform api unith ai/head/ 1234567890 1234567890 /conversation settings' \\ h 'accept application/json' \\ h 'authorization bearer abcdef123456 abcdef123456 ' \\ h 'content type application/json' \\ d ' { "conversationsettings" { "tools" \[ { "name" "create ticket", "description" "opens a support ticket ", "parameters" \[ ], "url" "https //yourdomain com/api/ticket" } ] } { "conversationsettings" { "tools" \[ { "name" "create ticket", "description" "opens a support ticket ", "parameters" \[ ], "url" "https //yourdomain com/api/ticket" } ] } } ' once this is applied, the tool is immediately available to be triggered by the llm during conversations step 3 defining context in the prompt tools don’t activate themselves—you guide them the digital human decides when and how to use a tool based on your prompt the more specific your prompt, the better the tool works you must include instructions in the prompt that define declaring tools "you have access to one tool create ticket" what condition should trigger the tool “as soon as the user wants to report a problem ” which parameters to collect username = user's full name useremail = user's email address userissue = short description of the user's problem how/when to call the tool "as soon as the user provides the last missing parameter, immediately call the tool create ticket with the parameters username, useremail and userissue " any fallback or validation logic “if the user refuses to give an email, explain that it’s required ” ✅ good prompt available tools you have access to one tool \ create ticket this tool opens a support ticket with the user's information it requires the following parameters username = the user's full name useremail = the user's email address userissue = a short description of the problem trigger condition & parameter collection as soon as the user wants to report an issue—this could include phrases like “i need help”, “i have a problem”, “can i open a ticket?”, or similar—ask for the username, useremail and userissue tool activation logic once the user provides the last missing parameter, immediately call the tool create ticket with the parameters username, useremail and userissue fallback & validation if the user refuses to provide their email address, explain that it is required in order to open a support ticket if any of the required fields is missing or unclear, ask a follow up question to complete the information before triggering the tool 🚫 vague prompt you can use tools to help the user if they have a problem, try to collect their information and open a ticket important notes the llm can make inferred decisions —it doesn’t need fixed rules but benefits from examples or clear intent instructions the model only triggers a tool when the conditions are met , so you can avoid accidental calls