Hey everyone! I’ve been diving into LangGraph lately and it’s pretty amazing stuff. But I’m stuck on something that’s been bugging me.
How exactly does LangGraph figure out what each tool is supposed to do? I mean, what’s the best way to make sure the AI understands what my custom tools are for?
I’m thinking it probably just looks at the function name and maybe the parameters, right? Should I be creating really descriptive function names to spell out every detail? That seems like it could get messy fast.
Like, I saw this example somewhere:
@tool
def get_user_help(message: str) -> str:
"""Ask a human user for help with a task."""
response = pause_for_input({"message": message})
return response["answer"]
But how does the system know that get_user_help means it needs to stop and wait for someone to respond? It could just as easily think it means the human will do all the work instead of providing guidance.
I’m probably overthinking this, but I want to make sure I’m building tools that the AI can actually use properly. Any tips on making tool descriptions clearer?
The AI cares way more about docstrings than function names. When I started with LangGraph, I wrote vague descriptions and got weird behavior. You need to be explicit - what does the tool do, what inputs does it need, what comes out. In your example, “Ask a human user for help with a task” tells the system it’s about human interaction. I add context about when to use it too. Something like “Use this when you need human clarification or can’t proceed without more info” makes the intent crystal clear. The AI uses this semantic info to match tools to situations, not just pattern matching names.
Parameter typing matters way more than you’d think. I wasted weeks debugging tool calls that weren’t working, then realized LangGraph actually reads your type hints to figure out data flow. Your function signature creates constraints that guide the AI’s tool selection. When you write message: str, the system knows it needs string input. But Union[str, dict] or custom types? That gives the AI more ways to tackle the problem.
The real lightbulb moment was understanding LangGraph builds a decision tree from your available tools. It’s not just matching descriptions - it’s figuring out which tool combinations can solve the current state. In your example, the AI picks up on pause_for_input as a blocking operation because of how the docstring and function behavior work together. You can make this even clearer by adding workflow implications to your description.
What really helped my tool selection was adding constraint info. Don’t just describe what the tool does - include when NOT to use it. Something like “Only use when automated solutions have failed” gives the AI better decision boundaries.
I’ve hit this exact problem building internal tools at work. The magic happens in the docstring but there’s more to it.
LangGraph uses your function signature plus docstring to build a semantic map of your tool. The AI looks at description, parameter types, and return type for context.
In your example, “pause_for_input” in the implementation matters too. The AI picks up keywords that signal workflow interruption.
Adding examples in the docstring really helped me:
@tool
def get_user_help(message: str) -> str:
"""Pause execution and ask human for guidance.
Use when you need clarification or additional context.
Example: get_user_help("Should I delete these 500 files?")
"""
The AI learns from your parameter names too. “message” suggests communication, while “query” or “request” gives different context.
Being specific about expected workflow in the docstring makes a huge difference. Words like “pause”, “wait”, “interrupt” signal this tool changes execution flow.
Don’t overthink the function name. Focus on making the docstring tell a complete story about what happens when this tool runs.
docstrings are key in LangGraph! They explain tool fucntions in detail. while good names help, it’s all about the description. make them clear so the AI knows exzactly how to use your tools. don’t skip on clarity!