I’m working with Function Tools in Vertex AI Agent Builder and running into an issue with the response format. Based on the documentation, when my agent decides to call a tool, I should receive a toolCall object in the response like this:
{
"queryResult": {
"text": "what is the temperature in Seattle",
"languageCode": "en",
"responseMessages": [
{
"source": "VIRTUAL_AGENT",
"toolCall": {
"tool": "<tool-identifier>",
"action": "fetch-temperature",
"inputParameters": {
"city": "Seattle"
}
}
}
]
}
}
When I test this in the Agent Builder interface by asking “what’s the temperature today”, I can see the toolCall data in the network requests to dialogflow.clients6.google.com/v3alpha1/. Here’s what I get:
"queryResult": {
"text": "what's the temperature in Berlin?",
"languageCode": "en",
"responseMessages": [
{
"source": "VIRTUAL_AGENT",
"toolCall": {
"tool": "projects/myproject/locations/us/agents/abc123-def4-5678-9abc-def012345678/tools/xyz789-abc1-2345-6789-abcdef012345",
"action": "fetchTemperature",
"inputParameters": {
"city": "Berlin, Germany",
"scale": "fahrenheit"
}
}
}
]
}
But when I use Dialogflow Messenger integration, it makes calls to dialogflow.googleapis.com/v3/ and the toolCall is missing:
"queryResult": {
"text": "what's the temperature in Berlin?",
"languageCode": "en",
"responseMessages": [
{}
]
}
This causes problems because my session gets stuck waiting for tool execution results. I get an error saying the session is waiting for tool call results for the fetchTemperature action. Why do these two interfaces behave differently? How can I get the toolCall data when using Dialogflow Messenger?
sounds like a version mismatch. agent builder’s on v3alpha1 but messenger uses regular v3 - they don’t handle toolCall data the same way. try hitting the REST API directly or check messenger settings for a webhook option to intercept those calls.
This drove me absolutely nuts when I first ran into it. Dialogflow Messenger treats tool calls as internal operations and handles them automatically behind the scenes - that’s why you’re getting empty response messages instead of the toolCall object.
What fixed it for me was ditching the built-in messenger integration and setting up a custom webhook instead. Configure a webhook endpoint in your agent settings and all requests hit your server first. Now you can access the complete toolCall data before anything gets processed.
The webhook gets the full response structure with toolCall objects, you run your functions, then return results directly to the user. Completely bypasses the messenger’s automatic tool handling.
If you’re stuck using Dialogflow Messenger, you can monitor session state through the REST API separately. Poll for pending tool executions and inject results back into the conversation flow - but this is way more complex and less reliable than the webhook approach.
Yeah, classic Google inconsistency. Hit the same issue last year building a support bot.
Messenger integration wasn’t built for devs who need raw toolCall data. It’s designed as plug-and-play, handling everything internally.
What worked: ditch Messenger, use detectIntent API directly. You get full response parsing control and toolCall objects without filtering.
Set your frontend to call Dialogflow API directly using the same v3alpha1 endpoint Agent Builder uses. Consistent behavior, toolCall data every time.
For UI, style it like Messenger but handle conversation flow yourself. More upfront work, but you avoid these API quirks.
Actually prefer this approach now - you can add custom loading states during tool execution and handle errors way better than the default messenger widget.
The Problem:
You are experiencing inconsistencies in the response format from Google Dialogflow when using Function Tools in Vertex AI Agent Builder. When testing within the Agent Builder interface, the toolCall object is present in the response, allowing your agent to properly execute the tool. However, when using the Dialogflow Messenger integration, this crucial toolCall object is missing, causing your session to get stuck waiting for tool execution results and resulting in errors. The core issue is the difference in API versions and how they handle tool calls.
Understanding the “Why” (The Root Cause):
The discrepancy arises from fundamental differences in how the v3alpha1 (used by Agent Builder) and v3 (used by Dialogflow Messenger) APIs handle tool calls. Dialogflow Messenger, designed for a simpler user experience, handles tool executions internally and automatically. This means it doesn’t expose the toolCall object in the response to the client because it manages the execution behind the scenes. Agent Builder, on the other hand, provides developers with more explicit control and therefore makes the toolCall object available in the response. This inherent design difference is the root cause of the missing data.
Step-by-Step Guide:
-
Implement a Custom Webhook: The most robust solution is to bypass the Dialogflow Messenger’s built-in tool handling entirely. This involves setting up a custom webhook in your Dialogflow agent’s settings. All requests will now first hit your webhook endpoint. Your webhook will receive the full response, including the toolCall object. This enables you to process the tool call, execute your functions, and then return the results directly to the user. This approach gives you complete control over the process and is significantly more reliable than attempting to work around the limitations of the built-in Messenger integration. This requires setting up a server to handle the webhook requests.
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Verify Webhook Configuration: Double-check your webhook settings in Dialogflow. Ensure the URL is correct, the authentication method (e.g., using a service account key) is properly configured, and that your webhook endpoint is correctly handling the incoming requests and responding with the expected format. Pay close attention to the HTTP headers and body of both incoming requests and outgoing responses. Ensure your service account has the necessary Dialogflow permissions.
-
Handle Tool Execution in Your Webhook: Your webhook endpoint will need to extract the tool parameters from the toolCall object, execute your function (like your temperature fetching function – adapt this to your specific tool), and return a response that Dialogflow can understand. This will involve writing code to process the toolCall data and interact with your external service. Remember to handle potential errors gracefully, returning appropriate error messages to the user. The response must conform to the expected Dialogflow response format.
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Test Thoroughly: After implementing the webhook, rigorously test your agent using the Dialogflow Messenger integration to ensure the toolCall object is now correctly processed.
Common Pitfalls & What to Check Next:
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Authentication Errors: The most common issue is incorrect webhook authentication. Verify that your service account has the necessary Dialogflow permissions. Check your webhook’s authentication method (e.g., Bearer token, API key) and ensure it’s correctly configured both in Dialogflow and in your webhook code.
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Response Formatting: Ensure the response from your webhook conforms to the expected Dialogflow response format. An incorrectly formatted response can lead to unexpected behavior. Refer to the Dialogflow documentation for the correct response structure.
-
Error Handling: Implement robust error handling in your webhook to gracefully handle situations where tool execution fails. Log errors to help with debugging, and provide informative error messages to the user.
-
Rate Limiting: Be mindful of rate limits on both your webhook and the external services you’re calling (like your temperature API). Implement retry mechanisms and exponential backoff strategies to handle temporary service disruptions.
Still running into issues? Share your (sanitized) config files, the exact command you ran, and any other relevant details. The community is here to help!
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