Fixing Neuron AI Tool Calls With Null Parameters

Aug 5, 2025 by Kenji Nakamura 49 views

Fixing Null Parameter Values in Neuron AI Tool Calls

Introduction

Hey guys! Today, we're diving into a common issue faced when working with the Neuron AI tool, specifically when dealing with null parameter values in tool calls. This can be a real headache, especially when integrating Neuron AI with tools like Playwright MCP. We'll break down the problem, explore the expected behavior, and discuss a practical solution. So, let's get started and make sure those null values don't trip us up anymore!

Understanding the Bug: Null Parameter Values

So, what's the fuss about these null values? Well, when using Neuron AI, particularly with tools like the Playwright MCP, you might encounter a situation where the agent calls certain tools with parameters that have null values. Imagine this: your agent is trying to interact with a webpage using Playwright, but some of the parameters it sends along are just empty, like a ghost in the machine. This can lead to errors and unexpected behavior, and that's precisely what we're trying to avoid.

The problem manifests when the tool expects a specific type of value, such as a string, but receives a null instead. This mismatch causes the tool to throw an error, indicating an invalid type. For example, you might see an error like:

[
  {
    "code": "invalid_type",
    "expected": "string",
    "received": "null",
    "path": [
      "element"
    ],
    "message": "Expected string, received null"
  },
  {
    "code": "invalid_type",
    "expected": "string",
    "received": "null",
    "path": [
      "ref"
    ],
    "message": "Expected string, received null"
  }
]

This error message tells us that the tool expected a string for both the element and ref parameters, but it received null instead. It's like ordering a pizza with toppings and getting a plain crust – not quite what you expected!

Looking under the hood, the issue arises because Neuron AI is passing these null values in the tool request. Here’s an example of what that request might look like:

{
  "jsonrpc": "2.0",
  "id": 9,
  "method": "tools/call",
  "params": {
    "name": "browser_evaluate",
    "arguments": {
      "function": "() => document.querySelector('form[data-contact-form]') !== null",
      "element": null,
      "ref": null
    }
  }
}

Notice how the element and ref parameters are set to null. This is the root cause of the problem. When the tool receives these null values, it gets confused and throws an error because it's expecting actual string values.

Why Does This Happen?

You might be wondering why Neuron AI is sending these null values in the first place. Well, in many cases, these parameters are optional. If they're not needed for a particular tool call, you'd expect them to be ignored or filtered out. But, as we've seen, Neuron AI sometimes includes them with a null value, which leads to the error. It’s like inviting a guest to a party and then telling them there's nothing for them to do – a bit awkward!

To summarize, null parameter values in tool calls can cause significant issues when using Neuron AI, especially when integrating with tools like Playwright MCP. These nulls lead to type mismatch errors, disrupting the expected behavior of the tool. Understanding this problem is the first step in fixing it, so let's move on to how we expect things to work.

Expected Behavior: Ignoring Optional Arguments

Now that we've dissected the problem, let's talk about the ideal scenario. When we're working with tools and agents, we want things to be smooth and intuitive, right? So, what's the expected behavior when it comes to optional arguments in tool calls?

The key here is flexibility and efficiency. When an argument is optional and not needed for a particular tool call, it should simply be ignored or filtered out. Think of it like ordering a sandwich: if you don't ask for pickles, the sandwich maker shouldn't put them on. Similarly, if a tool doesn't need a specific parameter, Neuron AI shouldn't send it along with a null value.

In a perfect world, the tool should only receive the arguments that are actually required and have populated values. This keeps the tool call clean and focused, reducing the chances of errors and making the entire process more efficient. Imagine how much smoother your workflow would be if you didn't have to worry about these pesky null values causing problems!

Let’s break this down further. Consider the browser_evaluate tool we mentioned earlier. It might have optional parameters like element and ref. If these parameters aren't necessary for a specific evaluation, we expect Neuron AI to omit them from the tool call altogether. This means the JSON payload sent to the tool would look something like this:

{
  "jsonrpc": "2.0",
  "id": 9,
  "method": "tools/call",
  "params": {
    "name": "browser_evaluate",
    "arguments": {
      "function": "() => document.querySelector('form[data-contact-form]') !== null"
    }
  }
}

Notice that the element and ref parameters are completely absent. This is the desired behavior. By only sending the necessary information, we avoid the type mismatch errors and ensure the tool operates as expected. It's like giving the tool a clear, concise instruction without any unnecessary details.

Benefits of Expected Behavior

So, why is this expected behavior so important? Well, there are several key benefits:

Reduced Errors: By filtering out null values, we eliminate the risk of type mismatch errors. This leads to more reliable and stable tool calls.
Improved Efficiency: Sending only the necessary parameters makes the tool call more efficient. The tool doesn't have to waste time processing unnecessary information.
Cleaner Code: When the tool calls are cleaner and more focused, the overall code becomes easier to understand and maintain. This is a big win for long-term project health.
Better Integration: Adhering to this expected behavior ensures better integration with various tools and services. It creates a more consistent and predictable environment.

In summary, the expected behavior when dealing with optional arguments is to ignore or filter out any null values. This approach leads to fewer errors, improved efficiency, cleaner code, and better overall integration. By ensuring that only the required and populated arguments are sent to the tool, we can make our lives as developers much easier. Now, let’s look at how we can actually fix this issue in Neuron AI.

The Solution: Filtering Null Arguments in McpClient::callTool

Alright, let's get down to the nitty-gritty and talk about how we can actually fix this null parameter issue in Neuron AI. The good news is that there's a straightforward solution: we can filter out the null arguments before they're sent to the tool. It's like having a bouncer at the door of the tool call, making sure no unwanted guests (null values) get in!

The suggested fix involves modifying the McpClient::callTool function. This function is responsible for making the actual tool calls, so it's the perfect place to implement our filtering mechanism. By adding a simple filter here, we can ensure that only the necessary, non-null arguments are included in the request.

The specific location for this fix is in the \NeuronAI\MCP\McpClient::callTool file. If you're familiar with the codebase, you'll recognize this as the spot where the tool call is constructed and sent. It's like the command center for all tool-related communications.

How to Implement the Fix

So, how do we actually implement this fix? The core idea is to iterate through the arguments and remove any that have a null value. This can be achieved with a few lines of code. Here's a conceptual example of what the filtering process might look like in PHP (since Neuron AI is built with PHP):

<?php

namespace NeuronAI\MCP;

class McpClient
{
    // ... other code ...

    public function callTool(string $name, array $arguments)
    {
        // Filter out null arguments
        $filteredArguments = array_filter(
            $arguments,
            function ($value) {
                return $value !== null;
            }
        );

        // Construct the tool call payload with filtered arguments
        $payload = [
            'jsonrpc' => '2.0',
            'id' => $this->generateId(),
            'method' => 'tools/call',
            'params' => [
                'name' => $name,
                'arguments' => $filteredArguments,
            ],
        ];

        // ... rest of the callTool logic ...
    }

    // ... other code ...
}

In this example, we're using the array_filter function to create a new array $filteredArguments that only includes the arguments with non-null values. The anonymous function function ($value) { return $value !== null; } acts as a filter, ensuring that only values that are not null are included in the new array.

This simple addition can make a world of difference. By filtering out the null arguments, we ensure that the tool receives a clean and valid set of parameters. This eliminates the type mismatch errors we discussed earlier and makes the tool calls much more reliable.

Why This Solution Works

So, why is this filtering approach so effective? Well, it addresses the root cause of the problem. By preventing null values from being sent to the tool, we avoid the errors that occur when the tool expects a specific type but receives a null. It’s like putting a shield up to protect the tool from bad data.

Additionally, this solution is non-invasive. It doesn't require us to change the tool's code or modify how it handles parameters. Instead, we're simply ensuring that the tool receives the correct input. This makes the fix both efficient and safe.

In summary, filtering null arguments in the McpClient::callTool function is a practical and effective solution for the null parameter issue in Neuron AI. By implementing this fix, we can ensure that our tool calls are clean, reliable, and error-free. Now that we know how to fix the problem, let's consider the Neuron AI version this applies to.

Neuron AI Version and Additional Context

To ensure we're all on the same page, let's talk about the specific version of Neuron AI where this issue was observed. It's crucial to know this so you can verify if you're running a version that might be affected by this bug.

The Neuron AI version in question is Version 1.16.7. If you're using this version, there's a good chance you might encounter the null parameter issue we've been discussing. It's like knowing the serial number of a potentially faulty device – it helps you identify if you're at risk.

Why Version Information Matters

So, why is knowing the version number so important? Well, software is constantly evolving. New versions are released with bug fixes, performance improvements, and new features. If you're running an older version, you might be missing out on important fixes or improvements.

In this case, if you're on Version 1.16.7, you know that this specific null parameter issue is something you need to be aware of. You can then take the necessary steps to implement the fix we discussed earlier or consider upgrading to a newer version that might include the fix.

Additional Context and Considerations

Beyond the version number, there's some additional context that's worth considering. As mentioned earlier, this issue was observed when using Neuron AI with the Playwright MCP. Playwright is a powerful tool for automating browser interactions, and MCP (presumably standing for something like