> ## Documentation Index
> Fetch the complete documentation index at: https://docs.fireworks.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Tool Calling
> Connect models to external tools and APIs
Tool calling (also known as function calling) enables models to intelligently select and use external tools based on user input. You can build agents that access APIs, retrieve real-time data, or perform actions—all through [OpenAI-compatible](https://platform.openai.com/docs/guides/function-calling) tool specifications.
**How it works:**
1. Define tools using [JSON Schema](https://json-schema.org/learn/getting-started-step-by-step) (name, description, parameters)
2. Model analyzes the query and decides whether to call a tool
3. If needed, model returns structured tool calls with parameters
4. You execute the tool and send results back for the final response
## Quick example
Define tools and send a request - the model will return structured tool calls when needed:
Initialize the client:
```python Python (Fireworks SDK) theme={null}
from fireworks import Fireworks
client = Fireworks()
```
```python Python (OpenAI SDK) theme={null}
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ.get("FIREWORKS_API_KEY"),
base_url="https://api.fireworks.ai/inference/v1"
)
```
Define the tools and make the request:
```python theme={null}
tools = [{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get the current weather for a location",
"parameters": {
"type": "object",
"properties": {
"location": {"type": "string", "description": "City name"}
},
"required": ["location"]
}
}
}]
response = client.chat.completions.create(
model="accounts/fireworks/models/kimi-k2-instruct-0905",
messages=[{"role": "user", "content": "What's the weather in San Francisco?"}],
tools=tools,
temperature=0.1
)
print(response.choices[0].message.tool_calls)
# Output: [ChatCompletionMessageToolCall(id='call_abc123', function=Function(arguments='{"location":"San Francisco"}', name='get_weather'), type='function')]
```
For best results with tool calling, use a low temperature (0.0-0.3) to reduce hallucinated parameter values and ensure more deterministic tool selection.
```python theme={null}
import json
# Step 1: Define your tools
tools = [{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get the current weather for a location",
"parameters": {
"type": "object",
"properties": {
"location": {"type": "string", "description": "City name"},
"unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
},
"required": ["location"]
}
}
}]
# Step 2: Send initial request
messages = [{"role": "user", "content": "What's the weather in San Francisco?"}]
response = client.chat.completions.create(
model="accounts/fireworks/models/kimi-k2-instruct-0905",
messages=messages,
tools=tools,
temperature=0.1
)
# Step 3: Check if model wants to call a tool
if response.choices[0].message.tool_calls:
# Step 4: Execute the tool
tool_call = response.choices[0].message.tool_calls[0]
# Your actual tool implementation
def get_weather(location, unit="celsius"):
# In production, call your weather API here
return {"temperature": 72, "condition": "sunny", "unit": unit}
# Parse arguments and call your function
function_args = json.loads(tool_call.function.arguments)
function_response = get_weather(**function_args)
# Step 5: Send tool response back to model
messages.append(response.choices[0].message) # Add assistant's tool call
messages.append({
"role": "tool",
"tool_call_id": tool_call.id,
"content": json.dumps(function_response)
})
# Step 6: Get final response
final_response = client.chat.completions.create(
model="accounts/fireworks/models/kimi-k2-instruct-0905",
messages=messages,
tools=tools,
temperature=0.1
)
print(final_response.choices[0].message.content)
# Output: "It's currently 72°F and sunny in San Francisco."
```
## Defining tools
Tools are defined using [JSON Schema](https://json-schema.org/understanding-json-schema/reference) format. Each tool requires:
* **name**: Function identifier (a-z, A-Z, 0-9, underscores, dashes; max 64 characters)
* **description**: Clear explanation of what the function does (used by the model to decide when to call it)
* **parameters**: JSON Schema object describing the function's parameters
Write detailed descriptions and parameter definitions. The model relies on these to select the correct tool and provide appropriate arguments.
### Parameter types
JSON Schema supports: `string`, `number`, `integer`, `object`, `array`, `boolean`, and `null`. You can also:
* Use `enum` to restrict values to specific options
* Mark parameters as `required` or optional
* Provide descriptions for each parameter
```python theme={null}
tools = [
{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get current weather for a location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "City name, e.g. San Francisco"
},
"unit": {
"type": "string",
"enum": ["celsius", "fahrenheit"],
"description": "Temperature unit"
}
},
"required": ["location"]
}
}
},
{
"type": "function",
"function": {
"name": "search_restaurants",
"description": "Search for restaurants by cuisine type",
"parameters": {
"type": "object",
"properties": {
"cuisine": {
"type": "string",
"description": "Type of cuisine (e.g., Italian, Mexican)"
},
"location": {
"type": "string",
"description": "City or neighborhood"
},
"price_range": {
"type": "string",
"enum": ["$", "$$", "$$$", "$$$$"]
}
},
"required": ["cuisine", "location"]
}
}
}
]
```
## Additional configurations
### tool\_choice
The [`tool_choice`](/api-reference/post-chatcompletions) parameter controls how the model uses tools:
* **`auto`** (default): Model decides whether to call a tool or respond directly
* **`none`**: Model will not call any tools
* **`required`**: Model must call at least one tool
* **Specific function**: Force the model to call a particular function
```python theme={null}
# Force a specific tool
response = client.chat.completions.create(
model="accounts/fireworks/models/kimi-k2-instruct-0905",
messages=[{"role": "user", "content": "What's the weather?"}],
tools=tools,
tool_choice={"type": "function", "function": {"name": "get_weather"}},
temperature=0.1
)
```
Some models support parallel tool calling, where multiple tools can be called in a single response. Check the model's capabilities before relying on this feature.
## Streaming
Tool calls work with streaming responses. Arguments are sent incrementally as the model generates them:
```python theme={null}
import json
tools = [{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get the current weather for a city",
"parameters": {
"type": "object",
"properties": {
"city": {"type": "string", "description": "City name"},
"unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
},
"required": ["city"]
}
}
}]
stream = client.chat.completions.create(
model="accounts/fireworks/models/kimi-k2-instruct-0905",
messages=[{"role": "user", "content": "What's the weather in San Francisco?"}],
tools=tools,
stream=True,
temperature=0.1
)
# Accumulate tool call data
tool_calls = {}
for chunk in stream:
if chunk.choices[0].delta.tool_calls:
for tool_call in chunk.choices[0].delta.tool_calls:
index = tool_call.index
if index not in tool_calls:
tool_calls[index] = {"id": "", "name": "", "arguments": ""}
if tool_call.id:
tool_calls[index]["id"] = tool_call.id
if tool_call.function and tool_call.function.name:
tool_calls[index]["name"] = tool_call.function.name
if tool_call.function and tool_call.function.arguments:
tool_calls[index]["arguments"] += tool_call.function.arguments
if chunk.choices[0].finish_reason == "tool_calls":
for tool_call in tool_calls.values():
args = json.loads(tool_call["arguments"])
print(f"Calling {tool_call['name']} with {args}")
break
```
## Troubleshooting
* Check that your tool descriptions are clear and detailed
* Ensure the user query clearly indicates a need for the tool
* Try using `tool_choice="required"` to force tool usage
* Verify your model supports tool calling (check `supportsTools` field)
* Add more detailed parameter descriptions
* Use lower temperature (0.0-0.3) for more deterministic outputs
* Provide examples in parameter descriptions
* Use `enum` to constrain values to specific options
* Always validate tool call arguments before parsing
* Handle partial or malformed JSON gracefully in production
* Use try-catch blocks when parsing `tool_call.function.arguments`
## Next steps
Enforce JSON schemas for consistent responses
Learn about chat completions and other APIs
Deploy models on dedicated GPUs
Full chat completions API documentation