> ## Documentation Index
> Fetch the complete documentation index at: https://friendli.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.
# Tool Calling with Model APIs
> Implement tool calling with Friendli Model APIs. Tutorial covers defining tools, handling function calls, and building multi-turn agent loops.
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1. On your left sidebar, click the 'Model APIs' option to access the playground page.
2. You will see models that can be used as Model APIs. Select the one you want and click the endpoint.
3. Click the 'Tools' button, select the Search tool, and enter a query to see the response. 😀
## Step 2. Build a Custom Tool
Build your own creative tool. We will show you how to make a custom tool that retrieves temperature information. (Completed code snippet is provided at the bottom)
1. **Define a function for using as a custom tool**
```python theme={null}
def get_temperature(location: str) -> int:
"""Mock function that returns the city temperature"""
if "new york" in location.lower():
return 45
if "san francisco" in location.lower():
return 72
return 30
```
2. **Send a function calling inference request**
1. Add your input as a `user` role message.
2. The information about the custom function (e.g., `get_temperature`) goes into the tools option. JSON schema describes the function's parameters.
3. The response includes the `arguments` field, which are values extracted from the user's input that can be used as parameters of the custom function.
```python theme={null}
# pip install friendli
import os
from friendli import SyncFriendli
token = os.environ.get("API_KEY") or "YOUR_API_KEY"
client= SyncFriendli(token=token)
user_prompt = "I live in New York. What should I wear for today's weather?"
messages = [
{
"role": "user",
"content": user_prompt,
},
]
tools=[
{
"type": "function",
"function": {
"name": "get_temperature",
"description": "Get the temperature information in a given location.",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The name of current location e.g., New York",
},
},
},
},
},
]
chat = client.serverless.chat.complete(
model="zai-org/GLM-5.2",
messages=messages,
tools=tools,
temperature=0,
frequency_penalty=1,
)
print(chat)
```
3. **Generate the final response using the tool calling results**
1. Add the `tool_calls` response as an `assistant` role message.
2. Add the result obtained by calling the `get_temperature` function as a `tool` message to the Chat API again.
```python theme={null}
import json
func_kwargs = json.loads(chat.choices[0].message.tool_calls[0].function.arguments)
temperature_info = get_temperature(**func_kwargs)
messages.append(
{
"role": "assistant",
"tool_calls": [
tool_call.model_dump()
for tool_call in chat.choices[0].message.tool_calls
]
}
)
messages.append(
{
"role": "tool",
"content": str(temperature_info),
"tool_call_id": chat.choices[0].message.tool_calls[0].id
}
)
chat_w_info = client.serverless.chat.complete(
model="zai-org/GLM-5.2",
tools=tools,
messages=messages,
)
for choice in chat_w_info.choices:
print(choice.message.content)
```
* **Complete Code Snippet**
```python theme={null}
# pip install friendli
import json
import os
from friendli import SyncFriendli
token = os.environ.get("API_KEY") or "YOUR_API_KEY"
client = SyncFriendli(token=token)
user_prompt = "I live in New York. What should I wear for today's weather?"
messages = [
{
"role": "user",
"content": user_prompt,
},
]
tools=[
{
"type": "function",
"function": {
"name": "get_temperature",
"description": "Get the temperature information in a given location.",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The name of current location e.g., New York",
},
},
},
},
},
]
chat = client.serverless.chat.complete(
model="zai-org/GLM-5.2",
messages=messages,
tools=tools,
temperature=0,
frequency_penalty=1,
)
def get_temperature(location: str) -> int:
"""Mock function that returns the city temperature"""
if "new york" in location.lower():
return 45
if "san francisco" in location.lower():
return 72
return 30
func_kwargs = json.loads(chat.choices[0].message.tool_calls[0].function.arguments)
temperature_info = get_temperature(**func_kwargs)
messages.append(
{
"role": "assistant",
"tool_calls": [
tool_call.model_dump()
for tool_call in chat.choices[0].message.tool_calls
]
}
)
messages.append(
{
"role": "tool",
"content": str(temperature_info),
"tool_call_id": chat.choices[0].message.tool_calls[0].id
}
)
chat_w_info = client.serverless.chat.complete(
model="zai-org/GLM-5.2",
tools=tools,
messages=messages,
)
for choice in chat_w_info.choices:
print(choice.message.content)
```
## Congratulations
Following the above instructions, we've experienced the whole process of defining and using a custom tool to generate an accurate and rich answer from LLM models!
Brainstorm creative ideas for your agent by reading our blog articles!
* [**Building an AI Agent for Google Calendar**](https://friendli.ai/blog/ai-agent-google-calendar){/* - [**Hassle-free LLM Fine-tuning with FriendliAI and Weights & Biases**](https://friendli.ai/blog/llm-fine-tuning-friendliai-wandb) */}
* [**Building AI Agents Using Function Calling with LLMs**](https://friendli.ai/blog/ai-agents-function-calling)
* [**Function Calling: Connecting LLMs with Functions and APIs**](https://friendli.ai/blog/llm-function-calling)