Tool Calling Tutorial

Tool calling in the UI

1. Load a model with tool-calling support

Load a model with tool-calling support from the Model tab.

2. Select tools

In the chat sidebar, check the tools you want the model to use:

• web_search: Search the web using DuckDuckGo.
• fetch_webpage: Fetch the content of a URL.
• calculate: Evaluate math expressions.
• get_datetime: Get the current date and time.
• roll_dice: Roll dice.

3. Chat

Send a message as usual. When the model decides it needs a tool, it will call it automatically. You will see each tool call and its result in a collapsible accordion inside the chat message.

The model may call multiple tools in sequence before giving its final answer.

Writing custom tools

Each tool is a single .py file in user_data/tools/. It needs two things:

1. A tool dictionary that describes the function (name, description, parameters).
2. An execute(arguments) function that runs it and returns the result.

Here is a minimal example (user_data/tools/get_datetime.py):

from datetime import datetime

tool = {
    "type": "function",
    "function": {
        "name": "get_datetime",
        "description": "Get the current date and time.",
        "parameters": {
            "type": "object",
            "properties": {},
        }
    }
}


def execute(arguments):
    now = datetime.now()
    return {"date": now.strftime("%Y-%m-%d"), "time": now.strftime("%I:%M %p")}

An example with parameters (user_data/tools/roll_dice.py):

import random

tool = {
    "type": "function",
    "function": {
        "name": "roll_dice",
        "description": "Roll one or more dice with the specified number of sides.",
        "parameters": {
            "type": "object",
            "properties": {
                "count": {"type": "integer", "description": "Number of dice to roll.", "default": 1},
                "sides": {"type": "integer", "description": "Number of sides per die.", "default": 20},
            },
        }
    }
}


def execute(arguments):
    count = max(1, min(arguments.get("count", 1), 1000))
    sides = max(2, min(arguments.get("sides", 20), 1000))
    rolls = [random.randint(1, sides) for _ in range(count)]
    return {"rolls": rolls, "total": sum(rolls)}

You can open the built-in tools in user_data/tools/ for more examples.

MCP servers

You can connect to MCP (Model Context Protocol) servers to use their tools alongside local ones. Both HTTP and stdio servers are supported.

HTTP servers

In the chat sidebar, open the MCP servers accordion and enter one server URL per line. For servers that require authentication, append headers after the URL separated by commas:

https://example.com/mcp
https://other.com/mcp,Authorization: Bearer sk-xxx

Stdio servers

Stdio MCP servers run as local subprocesses. To configure them, create a user_data/mcp.json file using the standard format (compatible with Claude Desktop, Cursor, and LM Studio):

{
    "mcpServers": {
        "filesystem": {
            "command": "npx",
            "args": ["-y", "@modelcontextprotocol/server-filesystem", "/path/to/allowed/dir"]
        },
        "another-server": {
            "command": "python3",
            "args": ["-m", "my_mcp_server", "--flag", "value"],
            "env": {
                "API_KEY": "your-key-here"
            }
        }
    }
}

The file is detected automatically and a warning is printed at startup when it is found.

Quick test example: Install npx (comes with Node.js), then create user_data/mcp.json with:

{
    "mcpServers": {
        "filesystem": {
            "command": "npx",
            "args": ["-y", "@modelcontextprotocol/server-filesystem", "/tmp/folder"]
        }
    }
}

Create the target directory (mkdir -p /tmp/folder), start the web UI, load a model with tool-calling support, and try asking: "What files are in /tmp/folder?" or "Write a file called notes.txt in /tmp/folder containing 'MCP is working'".

Tool priority

All tools from the configured servers are automatically discovered and made available to the model during generation. If an MCP tool has the same name as a selected local tool, the local tool takes priority.

Tool calling over the API

Tool calling over the API follows the OpenAI API convention. Define your tools, send them with your messages, and handle tool calls in a loop until the model gives a final answer.

import json
import requests

url = "http://127.0.0.1:5000/v1/chat/completions"

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get the current weather for a given location.",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {"type": "string", "description": "City name"},
                },
                "required": ["location"]
            }
        }
    }
]


def execute_tool(name, arguments):
    if name == "get_weather":
        return {"temperature": "14°C", "condition": "partly cloudy"}
    return {"error": f"Unknown tool: {name}"}


messages = [{"role": "user", "content": "What's the weather like in Paris?"}]

for _ in range(10):
    response = requests.post(url, json={"messages": messages, "tools": tools}).json()
    choice = response["choices"][0]

    if choice["finish_reason"] == "tool_calls":
        messages.append({
            "role": "assistant",
            "content": choice["message"]["content"],
            "tool_calls": choice["message"]["tool_calls"],
        })

        for tool_call in choice["message"]["tool_calls"]:
            name = tool_call["function"]["name"]
            arguments = json.loads(tool_call["function"]["arguments"])
            result = execute_tool(name, arguments)
            print(f"Tool call: {name}({arguments}) => {result}")

            messages.append({
                "role": "tool",
                "tool_call_id": tool_call["id"],
                "content": json.dumps(result),
            })
    else:
        print(f"\nAssistant: {choice['message']['content']}")
        break

Supported models

The following models are supported:

• Qwen 3.5
• GPT-OSS
• Mistral Small / Devstral
• DeepSeek V3
• Kimi-K2
• MiniMax-M2.5
• GLM-5
• Llama 4

Other models that output tool calls as JSON (inside XML tags, code blocks, or plain JSON) are also supported through a generic fallback parser.