Annotate a function for use in tool calls, by providing a name, description, and type definition for the arguments.
Learn more in vignette("tool-calling")
.
Usage
tool(
fun,
description,
...,
arguments = list(),
name = NULL,
convert = TRUE,
annotations = list(),
.name = deprecated(),
.description = deprecated(),
.convert = deprecated(),
.annotations = deprecated()
)
Arguments
- fun
The function to be invoked when the tool is called. The return value of the function is sent back to the chatbot.
Expert users can customize the tool result by returning a ContentToolResult object.
- description
A detailed description of what the function does. Generally, the more information that you can provide here, the better.
- ...
- arguments
A named list that defines the arguments accepted by the function. Each element should be created by a
type_*()
function (orNULL
if you don't want the LLM to use that argument).- name
The name of the function. This can be omitted if
fun
is an existing function (i.e. not defined inline).- convert
Should JSON inputs be automatically convert to their R data type equivalents? Defaults to
TRUE
.- annotations
Additional properties that describe the tool and its behavior. Usually created by
tool_annotations()
, where you can find a description of the annotation properties recommended by the Model Context Protocol.- .name, .description, .convert, .annotations
ellmer 0.3.0
In ellmer 0.3.0, the definition of the tool()
function changed quite
a bit. To make it easier to update old versions, you can use an LLM with
the following system prompt
Help the user convert an ellmer 0.2.0 and earlier tool definition into a
ellmer 0.3.0 tool definition. Here's what changed:
* All arguments, apart from the first, should be named, and the argument
names no longer use `.` prefixes. The argument order should be function,
name (as a string), description, then arguments, then anything
* Previously `arguments` was passed as `...`, so all type specifications
should now be moved into a named list and passed to the `arguments`
argument. It can be omitted if the function has no arguments.
```R
# old
tool(
add,
"Add two numbers together"
x = type_number(),
y = type_number()
)
# new
tool(
add,
name = "add",
description = "Add two numbers together",
arguments = list(
x = type_number(),
y = type_number()
)
)
```
Don't respond; just let the user provide function calls to convert.
See also
Other tool calling helpers:
tool_annotations()
,
tool_reject()
Examples
# First define the metadata that the model uses to figure out when to
# call the tool
tool_rnorm <- tool(
rnorm,
description = "Draw numbers from a random normal distribution",
arguments = list(
n = type_integer("The number of observations. Must be a positive integer."),
mean = type_number("The mean value of the distribution."),
sd = type_number("The standard deviation of the distribution. Must be a non-negative number.")
)
)
tool_rnorm(n = 5, mean = 0, sd = 1)
#> [1] -1.400043517 0.255317055 -2.437263611 -0.005571287 0.621552721
chat <- chat_openai()
#> Using model = "gpt-4.1".
# Then register it
chat$register_tool(tool_rnorm)
# Then ask a question that needs it.
chat$chat("Give me five numbers from a random normal distribution.")
#> Here are five numbers from a random normal distribution (mean = 0,
#> standard deviation = 1):
#>
#> 1. 1.1484
#> 2. -1.8218
#> 3. -0.2473
#> 4. -0.2442
#> 5. -0.2827
# Look at the chat history to see how tool calling works:
chat
#> <Chat OpenAI/gpt-4.1 turns=4 tokens=248/83 $0.00>
#> ── user [96] ──────────────────────────────────────────────────────────
#> Give me five numbers from a random normal distribution.
#> ── assistant [22] ─────────────────────────────────────────────────────
#> [tool request (call_i23r8dfi9bjtZStFRX69Zz40)]: rnorm(n = 5L, mean = 0L, sd = 1L)
#> ── user [34] ──────────────────────────────────────────────────────────
#> [tool result (call_i23r8dfi9bjtZStFRX69Zz40)]: [1.1484,-1.8218,-0.2473,-0.2442,-0.2827]
#> ── assistant [61] ─────────────────────────────────────────────────────
#> Here are five numbers from a random normal distribution (mean = 0, standard deviation = 1):
#>
#> 1. 1.1484
#> 2. -1.8218
#> 3. -0.2473
#> 4. -0.2442
#> 5. -0.2827
# Assistant sends a tool request which is evaluated locally and
# results are sent back in a tool result.