Tool Calling

Programming with LLMs in R and Python

posit::conf(2026)

2026-09-14

Tool calling

A robot wearing a tool belt, selects a tool from a wall of futuristic space tools. A sign above the wall reads “Tools”.

Recall: How do LLMs work?

1. You write some words

2. The LLM writes some more words

3. You use those words

On its own, can a word-writing model…

• Access the internet?

• Send an email?

• Interact with the world?

Let’s try it

library(ellmer)

chat <- chat("openai/gpt-4.1-nano")

chat$chat("What's the weather like in Houston, TX?")
chat$chat("Who are the keynote speakers at posit::conf(2026)?")
chat$chat("What day is it?")

import chatlas

chat = chatlas.Chat(model="openai/gpt-4.1-nano")
chat.chat("What's the weather like in Houston, TX?")
chat.chat("Who are the keynote speakers at posit::conf(2026)?")
chat.chat("What day is it?")

Run these in the terminal.

Ask: how could we get the model to know these answers? Let’s start with the easiest one: what day is it?

Answer: we could just tell it in the system prompt.

What about keynote speakers? We could include that in the system prompt too. But what if the schedule changes?

What about the weather? The model can’t know that because it’s not connected to the internet.

Tools

a.k.a. functions, tool calling or function calling

• Bring real-time or up-to-date information to the model

• Let the model interact with the world

Tools are extra capabilities that you give to the LLM, like the ability to search the posit::conf(2026) schedule. These capabilities are things that the model can’t do on its own. The goal is let the model pull in real-time, or up-to-date, or high-quality information as needed, depending on the conversation.

They can also give the model new abilities to take action on behalf of the user.

Chatbot Systems

How do tool calls work?

What should I wear to posit::conf(2026) in Houston?

So how do we connect the LLM to the code that this data scientist has written (or could write) to give the model new abilities?

Human in the loop

👨‍💻 _demos/15_demo_manual-tools

Wait… I can write code!

👨‍💻 Work through the script to register the tool, then run the app to see it in action

👨‍💻 16_weather-tool-01-start.R → 16_weather-tool-app.R

Live-code the demo script from -start to -end to register the tool with ellmer or chatlas, then just run the Shiny app (R or Python) so everyone sees the tool calls happening in the chat UI.

Zooming out again, the conversation starts with a user sending a message to the chatbot.

In this case, the user asks the model whether they can go to the pool today. (I live in Houston, TX.)

How much does the model know about the weather in Houston, TX?

What’s the weather like today in Houston, TX?

I’m unable to provide real-time weather updates. For the latest weather forecast in Houston, TX, please check a trusted weather service such as the National Weather Service, Weather.com, or a weather app on your device.

Go ahead and guess

Sure! Since it’s September in Houston, TX, you can generally expect hot and humid weather. Temperatures might range from the mid-80s to low 90s Fahrenheit (around 29-34°C). There could also be some afternoon thunderstorms or showers, which are common along the Gulf Coast. Keep a fan or air conditioning handy if you’re indoors, and stay hydrated!

Now imagine we’ve told the LLM that it has access to a tool called get_weather(). Knowing that it 1) needs to know the weather in Houston and 2) it doesn’t know the weather in Houston, TX; the model decides to use the weather tool.

It sends back a message that includes a tool call, basically instructions to call the get_weather() function with the argument 77010. Notice that the model does know that the Hilton Americas-Houston is in the 77010 ZIP code.

The programmer can then take that message from the LLM with the instructions to call the tool and they go off and run the code that satisfies the tool request, in this case querying the weather API for the weather in 77010 because that has been a stable fact for a long time.

The weather API sends back some data:

• The forecast is mostly sunny
• High of 98ºF
• Low of 78ºF

The programmer sends that data back to the LLM. Note that it’s often in a pretty raw format, like JSON. Not the kind of thing you’d want to read directly unless you’re the kind of person who likes reading computer data formats.

The LLM doesn’t mind JSON though, so it reads the data and generates a response that incorporates the data from the tool call.

Recap: Tool definitions in R

tool_get_weather <- tool(
  tool_fn,
  description = "How and when to use the tool",
  arguments = list(
    .... = type_string(),
    .... = type_integer(),
    .... = type_enum(
      c("choice1", "choice2"),
      required = FALSE
    )
  )
)

Recap: Tool definitions in Python

def get_weather(lat, lon):
    return NWS.GetCurrentForecast(lat, lon)

Recap: Tool definitions in Python

def get_weather(lat: float, lon: float):
    return NWS.GetCurrentForecast(lat, lon)

Recap: Tool definitions in Python

def get_weather(lat: float, lon: float):
    """
    Get forecast data for a specific latitude and longitude.

    Parameters
    ----------
    lat : str
        Latitude of the location.
    lon : str
        Longitude of the location.
    """
    return NWS.GetCurrentForecast(lat, lon)

Your Turn 17_quiz-game-2

1. I’ve set up a Quiz Game app with a function that plays a sound when called.

2. Your job: teach the model to play sounds at the right moments in the game.

3. You can use your prompt or switch _exercises → _solutions to use ours.

Follow up question: why send a string back to the LLM that says the sound was played?

Tool UI

A robot is standing in front of a display case, pointing at a holographic interface showing various tools and options. The robot appears to be selecting a tool from the interface.

Tool Annotations

• Provide hints to clients about tool behavior

• Help humans and AI systems make informed decisions about tool usage

• All properties are hints only - not guaranteed to be accurate

• MCP Tool Annotations Spec

Key Annotations

Property	Description
title	Human-readable tool name
icon	Icon shown next to the tool (shinychat only)
readOnlyHint	Tool doesn’t modify environment
destructiveHint	May perform destructive updates
idempotentHint	Repeated calls with same arguments return same
openWorldHint	Interacts with external entities vs. closed domain

Tool Annotations in R

tool_get_weather <- tool(
  get_weather,
  # description, arguments, ...
  annotations = tool_annotations(
    title = "Get Weather",
    readOnlyHint = TRUE,
    icon = fontawesome::fa_i("cloud-sun")
  )
)

Tool Annotations in Python

chat.register_tool(
    get_weather,
    annotations={
        "title": "Get Weather",
        "readOnlyHint": True,
        "extra": {
            "icon": faicons.fa_i("cloud-sun"),
        },
    },
)

Your Turn 18_quiz-game-3

1. Add tool annotations to our play_sound tools.

2. Pick an icon from FontAwesome free icons

Tools in Shiny

• Define the tool function inside the server

• You can update reactive values in the tool function!

• You can read reactive values, if you isolate() reads. (Be careful!)

• We will come back to this later 😉