How to Dynamically Trigger a Tool in Vertex AI Based on Attachment Count and Type?

[matteoem]

Hello everyone,

I'm currently developing an API that integrates with Gemini Vertex AI and makes use of function calling. One of the tools I’m implementing is designed to process attachments in batch, store them in an SQL table, and later query that table to return a formatted string for the frontend. However, I want this tool to be triggered dynamically and intelligently based on specific conditions rather than hardcoded logic.

What I Need

I want the tool to activate only when:

1. More than 5 attachments are present (excluding the message text).
2. The attachments belong to a specific category (e.g., in my use case, these are CVs).

Current Challenges

• Schema Constraints: I know that OpenAPI 3.0 (which Vertex AI follows) does not support minItems in the tool schema. This means I cannot enforce a minimum number of attachments at the schema level.
• Avoiding Hardcoded Logic: A simple if statement like the following inside the API would always trigger the tool when the condition is met, which is too rigid:

  def check_and_trigger_tool(prompt: list[Part]):
      if len(prompt) > 6:  # 1 text + 5 attachments
          return attachmentProccessorSupportTool(prompt)
      return "Tool not triggered: Less than 5 attachments present."

  The problem with this approach is that it blindly triggers based only on the number of attachments, without considering their type.
• Triggering Based on Content, Not Schema: Since function calling in Gemini is designed to trigger based on the text content of the prompt or the function's description, it is unclear how to dynamically activate a tool based on structured data like the number or type of file attachments.

What I’ve Tried So Far

• Describing the behavior in the tool’s description: I have explicitly stated that the tool should trigger when there are more than 5 attachments of the correct type. However, Gemini does not seem to infer this from the description alone.
• Structuring the schema properly: The tool’s parameter schema defines an array of Part objects, where each part contains metadata like MIME type and content. However, the schema itself does not allow specifying constraints like "only trigger if there are at least 5 CV files."

Ideal Solution

I am looking for a way to dynamically trigger the tool based on structured input (like the number and type of attachments) without hardcoding the logic inside the API.

• Is there a way to make Gemini understand trigger conditions based on structured data rather than textual descriptions?
• Does the function-calling mechanism provide any built-in support for intelligent tool selection based on structured inputs?
• If not, what would be the best approach to achieve this kind of context-aware tool activation dynamically?
• In general this matter can be generalized in: is there an advanced triggering method for tools using Gemini, or in general other LLMs?

Any insights or suggestions would be greatly appreciated

Thanks in advance!

[ibaui]

Hi @matteoem,

Welcome to Google Cloud Community!

Here are some potential solutions that might address your questions:

• Is there a way to make Gemini understand trigger conditions based on structured data rather than textual descriptions?

  Gemini's function calling is primarily driven by textual context and inference. While descriptions can guide it, they aren't a substitute for explicit logic. This is clear from the Examples of Function Calling, where the LLM selects a function based solely on understanding the user's textual prompt. All the examples lack specific conditions based on data to select one function or another.

• Does the function-calling mechanism provide any built-in support for intelligent tool selection based on structured inputs?

  The focus is on mapping text to function parameters after a tool has been selected, not on analyzing structured input for tool selection criteria. This is evidenced by the Parameter List documentation, which describes the structure of function parameters without providing any mechanism for using those parameters to influence which function is chosen in the first place. The available tools are inferred from the user query and not by hard logic rules that can be applied.

• If not, what would be the best approach to achieve this kind of context-aware tool activation dynamically?

  Since Gemini examples focus on prompt understanding, to introduce more advanced input logic, you necessarily need to pre-process the message contents to condition which tool is available. Thus, you need to implement a layer to filter functions before they are passed to the LLM.

• In general this matter can be generalized: is there an advanced triggering method for tools using Gemini, or in general other LLMs?

  Currently, the advanced triggering methods available still rely on the prompt. The main advantage is to structure the reply and make it follow a predefined structure. Advanced triggering depends on prompt contents, and there is not a built-in advanced and reliable automatic tool selection based on complex logic in the Gemini's function calling. The emphasis in Gemini's documentation on prompt engineering implicitly demonstrates that prompt content is the primary lever for controlling the LLM's behavior. It illustrates tool selection driven by the text of the user's prompt, not by complex, structured data analysis.

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