The best Node.JS library to help you performing a OpenAI SDK function calling. MCP Nicely supported.

``sh
npm i function-calling
`

🤩 Key Features

- 🔧 Automatically invoke tools.
- 🎨 Nicely MCP Support.
- 🔧→✉️→🔧→✉️ Continuously invoke tools in a loop until all tool call is satisfied.
- 🧐 Dynamically build tools so we can adjust tools for each call. (Such as modify parameters)
- 💰💰 Automatically summarize usages of internal chat completions.
- 💦 Streaming callbacks.

😎 Getting Started

There's a example presents a situation that involves continuous function callings.

The user requested LLM to automatically adjust AC temperature🧊🔥 based on recently weather info.

$3

`typescript
import {
functionalChatCompletion,
} from 'function-calling'
import OpenAI from 'openai'
async function chatWithGPT() {
const openai = new OpenAI({
baseURL: 'xxx',
apiKey: 'xxx',
})
const { usage, messages } =
await functionalChatCompletion(openai, {
mcpClients: [yourMCPClientA,yourMCPClientB],// optional, all tools inside it will be automatically executed.
tools: [ // optional, your custom javascript tools.
getWeather, adjustAirConditioner
]
stream: true,
model: 'gpt-4.1',
onTextDelta: (text) => console.log(text),
messages: [
{
role: 'user',
content:
'Hello, please query weather and adjust my AC!',
},
],
})
}
`

The returning message will presents its internal calling procedure.

`typescript
const messages = [
{
role: 'assistant',
content: "I'll try my best to query weather info! ",
tool_call: [
{
id: 'abc',
type: 'function',
function: {
name: 'GetWeather',
arguments: "{ 'futureDays':7 }",
},
},
],
},
{
role: 'tool',
content: 'The weather info of 7 is XXXXX',
tool_call_id: 'abc',
},
{
role: 'assistant',
content:
"The temperature will drop in future 7 days, I'm increasing your AC temperature.",
tool_call: [
{
id: 'def',
type: 'function',
function: {
name: 'AdjustAirConditioner',
arguments: '{ temperature: 25 }',
},
},
],
},
{
role: 'tool',
content: 'Adjusted',
tool_call_id: 'def',
},
{
role: 'assistant',
content:
'All done, please feel free to enjoy all my services. What can I do for you next?',
},
]
`

#### Custom Script Tools

`typescript
import {
buildTool,
} from '@/index'
const getWeather = buildTool({
name: 'GetWeather',
description:
'Get the weather info of where the user is',
schema: z.object({
futureDays: z.number().positive(),
}),
func: async ({ futureDays }) => {
return The weather info of ${futureDays}
},
})
const adjustAirConditioner = buildTool({
name: 'AdjustAirConditioner',
description:"Adjust user's Air conditioner"
schema: z.object({
temperature: z.number().positive().min(18).max(27),
}),
func: async (args) => {
await externalLibrary.adjustACTemp(args.temperature)
},
})
`

#### MCP Tools

`typescript
import { getToolsOfMCPClient } from 'function-calling'
// Convert all mcp sdk format tools to
// function-calling's format. It will eventually
// convert to OpenAI format during executing.
const yourMCPTools = await getToolsOfMCPClient(yourClient)

// But you don't have to care about any further operation.
// Just passing it.
await functionalChatCompletion(openai, {
tools: [
getWeather, adjustAirConditioner, ...yourMCPTools
]
stream: true,
model: 'gpt-4.1',
onTextDelta: (text) => console.log(text),
messages: [
{
role: 'user',
content:
'Hello, please query weather and adjust my AC!',
},
],
})
`

#### Which Way?

Q: which way should I choose to pass mcp tools to LLM?

A: It depends on your requirements.

##### First Way

`typescript
const yourMCPTools = await getToolsOfMCPClient(yourClient)
`

then pass it

`typescript
{
//...
tools: [
getWeather,
adjustAirConditioner,
...yourMCPTools,
]
//...
}
`

#### Second Way

Our library will call getToolsOfMCPClient inside function.

`typescript
{
//...
clients: [yourClient]
tools: [
getWeather,
adjustAirConditioner,
...yourMCPTools,
]
//...
}
`

Contribution

$3

We recommend to use pnpm as your package manager.

`sh
pnpm build
`

$3

We are using vitest for unit tests.

`sh
pnpm test
``

$3

Once you have developed codes, please raise PR to dev branch.

All pull requests are welcomed.