---
title: "Component: Tool"
---

LangBot's built-in Local Agent calls tools to interact with the outside world during execution. Currently, adding tools is supported through both plugins and MCP.

## Adding Tool Components

A single plugin can contain any number of tools. Execute the command `lbp comp Tool` in the plugin directory and follow the prompts to enter the tool configuration.

```bash
➜  HelloPlugin > lbp comp Tool
Generating component Tool...
Tool name: get_weather_alerts
Tool description: Get weather alerts for a US state.
Component Tool generated successfully.
组件 Tool 生成成功。
```

This will generate `get_weather_alerts.yaml` and `get_weather_alerts.py` files in the `components/tools/` directory. The `.yaml` file defines the basic information for the tool, and the `.py` file is the tool handler:

```bash
➜  HelloPlugin > tree
...
├── components
│   ├── __init__.py
│   └── tools
│       ├── __init__.py
│       ├── get_weather_alerts.py
│       └── get_weather_alerts.yaml
...
```

## Manifest File: Tool

```yaml
apiVersion: v1  # Do not modify
kind: Tool  # Do not modify
metadata:
  name: get_weather_alerts  # Tool name, for LLM recognition
  label:
    en_US: GetWeatherAlerts  # Tool display name, shown in LangBot UI, supports multilingual
    zh_Hans: GetWeatherAlerts
  description:
    en_US: 'Get weather alerts for a US state.'  # Tool description, shown in LangBot UI, supports multilingual. Optional
    zh_Hans: '获取美国某个州的天气预警'
spec:
  parameters: []  # Tool parameters, values generated by LLM based on conversation context
  llm_prompt: 'Get weather alerts for a US state.'  # Tool prompt, for LLM to determine whether to call this tool
execution:
  python:
    path: get_weather_alerts.py  # Tool handler, do not modify
    attr: GetWeatherAlerts  # Tool handler class name, matches the class name in get_weather_alerts.py
```

## Plugin Processing

The following code is generated by default (`components/tools/<tool_name>.py`). You need to implement the calling and return logic for this tool in the `call` method of the `GetWeatherAlerts` class.

```python
# Auto generated by LangBot Plugin SDK.
# Please refer to https://langbot.app/docs/en/plugin/dev/tutor.html for more details.
from __future__ import annotations

from typing import Any

from langbot_plugin.api.definition.components.tool.tool import Tool
from langbot_plugin.api.entities.builtin.provider import session as provider_session


class GetWeatherAlerts(Tool):

    async def call(self, params: dict[str, Any], session: provider_session.Session, query_id: int) -> str:
        """Fill your tool code here"""
        return {}
```

In the `call` method, implement this tool:

<Info>
The weather retrieval tool example comes from MCP's [Server Writing Example](https://modelcontextprotocol.io/quickstart/server).
</Info>

```python
from __future__ import annotations

from typing import Any

from langbot_plugin.api.definition.components.tool.tool import Tool
from langbot_plugin.api.entities.builtin.provider import session as provider_session
import httpx

# Constants
NWS_API_BASE = "https://api.weather.gov"
USER_AGENT = "weather-app/1.0"


async def make_nws_request(url: str) -> dict[str, Any] | None:
    """Make a request to the NWS API with proper error handling."""
    headers = {
        "User-Agent": USER_AGENT,
        "Accept": "application/geo+json"
    }
    async with httpx.AsyncClient() as client:
        try:
            response = await client.get(url, headers=headers, timeout=30.0)
            response.raise_for_status()
            return response.json()
        except Exception:
            return None

def format_alert(feature: dict) -> str:
    """Format an alert feature into a readable string."""
    props = feature["properties"]
    return f"""
Event: {props.get('event', 'Unknown')}
Area: {props.get('areaDesc', 'Unknown')}
Severity: {props.get('severity', 'Unknown')}
Description: {props.get('description', 'No description available')}
Instructions: {props.get('instruction', 'No specific instructions provided')}
"""

class GetWeatherAlerts(Tool):

    async def call(self, params: dict[str, Any], session: provider_session.Session, query_id: int) -> str:
        """Fill your tool code here"""
        state = params.get("state", "CA")

        url = f"{NWS_API_BASE}/alerts/active/area/{state}"
        data = await make_nws_request(url)

        if not data or "features" not in data:
            return "Unable to fetch alerts or no alerts found."

        if not data["features"]:
            return "No active alerts for this state."

        alerts = [format_alert(feature) for feature in data["features"]]
        return "\n---\n".join(alerts)
```

In this tool, we need to get the `state` parameter from `params` and call the NWS API to get weather alerts for that state, so we need to define the `state` parameter in the `parameters` of the manifest file.

The `parameters` format follows [JSON Schema](https://json-schema.org/docs), is supported by [OpenAI Function Calling feature](https://platform.openai.com/docs/guides/function-calling#defining-functions), and its root type is fixed as `type: object`. Please add parameters in `properties` and add required parameter descriptions in `required`.

```yaml
...
spec:
  parameters:
    type: object  # Do not modify
    properties:  # Do not modify, add parameters here
      state:
        type: string
        description: 'Two-letter US state code (e.g. CA, NY)'
    required:  # Add required parameter descriptions here
      - state
...
```

This tool also uses the `httpx` library, so we need to add the `httpx` dependency in `requirements.txt` in the plugin directory.

```txt
# HelloPlugin/requirements.txt
langbot-plugin  # Already exists by default
httpx
```

Now execute the command `lbp run` in the plugin directory to start debugging. Configure a model that supports tool calling in LangBot and select to use that model on the corresponding pipeline to use this tool.

<img width="600" src="/images/zh/plugin/dev/components/tool_use_in_chat.png" />

## One installation, many usages

A single Tool is called by **several processors and conversations** (one event can trigger several plugin processors):

- Read config per call (`self.get_plugin_config()` or `ctx.config`); never read it in `initialize()` and cache it on the instance.
- Never leave call arguments, results or conversation data on instance fields or module-level variables; take the conversation identity from the current call's `session` / `query_id`.
- Concurrent calls share the same instance, so instance fields get interleaved writes. Data that must survive calls belongs in Host storage, keyed with the corresponding processor or conversation identity.
- If you keep per-installation process-local caches, release them in `on_installation_revoked(binding)`.

See [Certified plugins and shared runtime](/en/plugin/certified-plugins) for the full specification.

## What's Next

You have learned the basic usage of tools. Next, you can:

- See how Tool receives [`session` and `query_id`](/en/plugin/dev/apis/agent-run#tool).
- Check out [Plugin Common APIs](/en/plugin/dev/apis/common)