Google: Gemini 3.1 Pro Preview Custom Tools

Gemini 3.1 Pro Preview Custom Tools implements a specialized tool-routing layer that analyzes user intents and selects the most efficient third-party tool or API instead of defaulting to a generic bash execution tool. The model uses semantic understanding of task requirements to route requests to domain-specific tools (e.g., image processing libraries, data transformation services) rather than shell commands, reducing execution overhead and improving reliability. This is achieved through a learned preference mechanism that weights tool selection based on task type, available tool capabilities, and execution efficiency metrics.

Gemini 3.1 Pro Preview Custom Tools accepts and processes multiple input modalities (text, images, audio, video) as context for tool selection and invocation decisions. The model analyzes multimodal inputs to understand task requirements, then routes to appropriate tools with extracted context. For example, an image input could trigger image processing tools, while audio might route to transcription or analysis services. The implementation uses unified embedding and attention mechanisms to fuse modality-specific representations before tool selection.

Gemini 3.1 Pro Preview Custom Tools implements error handling and recovery mechanisms for failed tool invocations. When a tool call fails, the model can analyze the error, attempt alternative tools, adjust parameters, or request clarification from the user. This is implemented through error feedback loops where tool execution errors are returned to the model, which then reasons about recovery strategies. The model can retry with different parameters, fall back to alternative tools, or escalate to the user if recovery is not possible.

Gemini 3.1 Pro Preview Custom Tools optimizes token usage for tool invocation by selectively including only relevant context in tool calls and responses. The model uses attention mechanisms to identify which parts of the conversation history, tool results, and user input are most relevant to the current tool invocation, then includes only that context in the API call. This reduces token consumption and latency compared to including full conversation history in every tool call. Token optimization is transparent to the user but can significantly reduce API costs.

Gemini 3.1 Pro Preview Custom Tools implements OpenAI-compatible and Google-native tool schema formats for function calling, with built-in validation of tool invocation parameters against declared schemas. The model generates structured tool calls that include function name, parameters, and optional metadata, with the runtime validating parameter types, required fields, and constraints before execution. This prevents malformed tool invocations and ensures type safety across heterogeneous tool ecosystems.

Gemini 3.1 Pro Preview Custom Tools maintains conversation history and uses it to inform tool selection and parameter generation across multiple turns. The model tracks previous tool invocations, their results, and user feedback to make more contextually appropriate decisions in subsequent turns. For example, if a previous image analysis tool returned specific metadata, the model can use that context to select a more specialized tool in the next turn. This is implemented through a stateful conversation manager that preserves tool execution context and results.

Gemini 3.1 Pro Preview Custom Tools generates natural language text responses that can be augmented or informed by tool invocations. The model can decide to invoke tools mid-response generation to gather information, then incorporate tool results into the final text output. For example, when answering a question, the model might invoke a search tool to fetch current information, then synthesize that into a comprehensive text response. This is implemented through a streaming architecture that allows tool invocations to be interleaved with text generation.

Gemini 3.1 Pro Preview Custom Tools allows developers to define custom tools using standardized schema formats (OpenAI-compatible or Google-native), then register them with the model for use in tool selection and invocation. Tools are defined declaratively with name, description, parameters, and optional metadata, enabling the model to understand tool capabilities and make informed selection decisions. The registration process validates tool schemas and makes them available for the current conversation or session.

Gemini 3.1 Pro Preview Custom Tools can analyze and understand images, extracting visual information, text, objects, and semantic content. The model processes images using vision transformers and multimodal embeddings to generate detailed descriptions, identify elements, perform OCR, and answer questions about image content. This capability is integrated with tool selection, allowing image analysis results to inform which tools should be invoked next. For example, analyzing an image of a document could trigger OCR tools or document processing services.

Gemini 3.1 Pro Preview Custom Tools can process and analyze video content, extracting temporal information, identifying scenes, detecting objects across frames, and understanding video narratives. The model uses frame sampling and temporal attention mechanisms to understand video structure and content, then can route to video-specific tools (transcription, scene detection, object tracking) based on analysis results. Video analysis is integrated with tool selection to enable intelligent dispatch to specialized video processing services.

Gemini 3.1 Pro Preview Custom Tools can process audio files, perform speech-to-text transcription, identify speakers, detect audio events, and understand spoken content. The model uses acoustic embeddings and language models to transcribe speech, identify languages, and extract semantic meaning from audio. Audio analysis results can inform tool selection, routing to transcription services, speaker identification tools, or audio processing pipelines based on detected content and quality.

Gemini 3.1 Pro Preview Custom Tools implements chain-of-thought reasoning and task decomposition to plan multi-step tool workflows. The model breaks down complex user requests into sequences of tool invocations, reasoning about dependencies, data flow, and optimal ordering. For example, a request to 'analyze this document and extract key metrics' might be decomposed into: (1) OCR tool to extract text, (2) NLP tool to identify key metrics, (3) data formatting tool to structure results. This is implemented through explicit reasoning steps that are exposed to the user and can be inspected or modified.