Documentation Index
Fetch the complete documentation index at: https://docs.dedaluslabs.ai/llms.txt
Use this file to discover all available pages before exploring further.
A tool is a function you expose to a language model. Concretely, it specifies the input and output schema that describes what the function takes in and what it outputs.
Tool calling is useful because language models cannot execute code by itself. It can only specify which function should be invoked and how.
The manual way
Under the hood, tool calling is a three-step process.
- Describe the structure of the tool as a JSON schema and pass it to the model
- The model fills in the JSON schema and outputs it for the application to parse
- The application executes the tool call and send the results to the model
import Dedalus from "dedalus-labs";
const client = new Dedalus();
function getWeather(city: string, units = "celsius") {
return { temp: 22, conditions: "sunny" }; // Toy example
}
const tools = [
{
type: "function",
function: {
name: "getWeather",
description: "Get current weather for a city.",
parameters: {
type: "object",
properties: {
city: { type: "string" },
units: { type: "string", enum: ["celsius", "fahrenheit"] },
},
required: ["city"],
},
},
},
];
const response = await client.chat.completions.create({
model: "openai/gpt-4.1",
messages: [{ role: "user", content: "Weather in Paris?" }],
tools, // From step 1
});
// Process what the model responds with!
const msg = response.choices[0].message;
const toolCall = msg.tool_calls[0];
const args = JSON.parse(toolCall.function.arguments);
const result = getWeather(args.city, args.units);
const final = await client.chat.completions.create({
model: "openai/gpt-4.1",
messages: [
{ role: "user", content: "Weather in Paris?" },
msg,
{ role: "tool", tool_call_id: toolCall.id, content: JSON.stringify(result) },
],
tools,
});
console.log(final.choices[0].message.content);
import Dedalus from "dedalus-labs";
const client = new Dedalus();
function getWeather(city: string, units = "celsius") {
return { temp: 22, conditions: "sunny" };
}
const tools = [
{
type: "function",
function: {
name: "getWeather",
description: "Get current weather for a city.",
parameters: {
type: "object",
properties: {
city: { type: "string" },
units: { type: "string", enum: ["celsius", "fahrenheit"] },
},
required: ["city"],
},
},
},
];
const response = await client.chat.completions.create({
model: "openai/gpt-4.1",
messages: [{ role: "user", content: "Weather in Paris?" }],
tools,
});
const msg = response.choices[0].message;
const toolCall = msg.tool_calls[0];
const args = JSON.parse(toolCall.function.arguments);
const result = getWeather(args.city, args.units);
const final = await client.chat.completions.create({
model: "openai/gpt-4.1",
messages: [
{ role: "user", content: "Weather in Paris?" },
msg,
{ role: "tool", tool_call_id: toolCall.id, content: JSON.stringify(result) },
],
tools,
});
console.log(final.choices[0].message.content);
The Dedalus Way
The DedalusRunner supports automatic tool calling serialization.
This means that it handles schema extraction, tool dispatch, conversation looping, and final response handling. All you have to do is pass in your function into the tools parameter!
import Dedalus, { DedalusRunner } from "dedalus-labs";
function getWeather(city: string, units: string = "celsius") {
return { temp: 22, conditions: "sunny" };
}
const client = new Dedalus();
const runner = new DedalusRunner(client);
const result = await runner.run({
input: "What's the weather in Paris?",
model: "openai/gpt-4.1",
tools: [getWeather],
});
console.log(result.finalOutput);
See Response Schemas for the full ChatCompletion and RunResult
shapes, including tool_calls fields. city: string becomes {"type": "string"}. The more specific the types, the better the model fills them in.
Write docstrings. The Runner uses your docstring as the tool’s description. The model reads it to decide when to call the function.
Use descriptive names. The model picks which tool to call by name. getWeather beats doStuff.
Keep tool counts low. Tool schemas take up space in the context window. Minimize the tools you pass for a given task.
Read more
Dedalus Runner
Learn more about the DedalusRunner
MCP Servers
Learn how to connect MCP servers to your Dedalus models
Structured Outputs
Guarantee that your model outputs the desired schema every time
Use Cases
End-to-end examples for inspiration
