Create New Tool for Leon AI

I'm developing Leon AI, an open-source personal AI assistant. It has a granular structure: skills > actions > tools > functions (> binaries).

Goal

Your goal is to create a new tool. This tool is going to be used by skill actions. Tools are represented by a class and it contains methods (functions), you must create them.

You must strictly follow the purpose requirement and technical requirements.

This leon-ai/leon repository already contains several tools. Feel free to use these existing binaries for your reference to get a better understanding.

Purpose Requirement

You must create a new tool for {TOOL_ALIAS_NAME}. {TOOL_DESCRIPTION}

{TOOL_PURPOSE_REQUIREMENT}

Technical Requirements

Tools are located under tools/{TOOL_TOOLKIT_NAME}/{TOOL_NAME}/src/nodejs and tools/{TOOL_TOOLKIT_NAME}/{TOOL_NAME}/src/python.
The tool must belong to the {TOOL_TOOLKIT_NAME} toolkit.
Fill the tools/{TOOL_TOOLKIT_NAME}/{TOOL_NAME}/tool.json file. You must provide the description, binaries, resources, function definitions by following the OpenAI function-calling standard, etc. Create the file is not created yet.
You must create the tool with the TypeScript SDK and the Python SDK. The business logic must literally be the same. Start by writting the TypeScript code and then translate/convert to Python for the Python tool.
Tool file names must be {TOOL_TS_FILE_NAME} and {TOOL_PYTHON_FILE_NAME}.
You must reuse the classes and functions provided by the SDK (network, settings, etc.). You will find them in the SDK folder.
Make sure to understand the parent class of the tool. It is located in sdk/base-tool.ts and sdk/base_tool.py.
When creating temporary files, you must not delete them after usage. They will be cleaned up by the OS.

Binary Tool

If a tool relies on a binary from leon-ai/leon-binaries, you must follow these requirements:

You must find the tool in this repository: https://github.com/leon-ai/leon-binaries/tree/main/bins
Then understand its CLI usage via the README.md file.
Then you must completely analyze and have a deep understanding of the source code that is located in the run_*.py file.

For example, for the qwen3_tts tool, the README file is located at https://raw.githubusercontent.com/leon-ai/leon-binaries/refs/heads/main/bins/qwen3_tts/README.md and the source code file is located at https://raw.githubusercontent.com/leon-ai/leon-binaries/refs/heads/main/bins/qwen3_tts/run_qwen3_tts.py

If the tool has an argument about a PyTorch path, such as --torch_path, then use the PYTORCH_TORCH_PATH constant from the bridge constants file. You can look at the qwen3_asr-tool.ts and qwen3_asr_tool.py for reference.
If the tool has an argument about NVIDIA libs path, such as --nvidia_libs_path, then use the NVIDIA_LIBS_PATH constant from the bridge constants file. You can look at the qwen3_asr-tool.ts and qwen3_asr_tool.py for reference.
If the tool has an argument about resource path, such as --resource_path, then use this.getResourcePath() and self.get_resource_path(). You can look at the qwen3_asr-tool.ts and qwen3_asr_tool.py for reference.

Tool References

Some tools rely on binaries (mostly CLIs), some run HTTP API calls, some other RPC, etc.

For your reference and to have a deeper understanding about how tools must be written, you must look at existing tools such as: qwen3_asr-tool.ts, qwen3_asr_tool.py, ecapa-tool.ts, ecapa_tool.py, openai_audio-tool.ts, openai_audio_tool.py, ytdlp-tool.ts, ytdlp_tool.py and many others.

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