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 bridges/nodejs/src/sdk/tools and bridges/python/src/sdk/tools.
• The tool must belong to the {TOOL_TOOLKIT_NAME} toolkit.
• Fill the bridges/toolkits/{TOOL_TOOLKIT_NAME}/tools/{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:

1. You must find the tool in this repository: https://github.com/leon-ai/leon-binaries/tree/main/bins
2. Then understand its CLI usage via the README.md file.
3. 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.