Technical notes for Gemini
Generate text, chat responses, and structured outputs using Google's multimodal Gemini AI models. Process and understand mixed inputs including text, images, audio, video, and PDF documents. Generate images via Imagen and native models, generate videos via Veo, and create music with granular creative controls. Execute Python code within the model environment. Produce text, image, video, and audio embeddings for semantic search and classification. Upload and manage files for use in prompts. Fine-tune models with custom training data. Use built-in tools including Google Search grounding, URL context fetching, and computer use automation. Cache context for repeated use across requests. Count tokens before sending requests. Stream real-time voice and video interactions via the Live API over WebSockets. Call external functions and chain multiple tool invocations to fulfill complex requests.
Frequently asked questions
Common questions about connecting Gemini to AI agents with Metorial.
Can Metorial connect Gemini to AI agents?
Yes. Metorial connects AI agents to Gemini through a governed integration layer, so teams can use the provider while keeping access controlled and observable.Does the Gemini integration work with MCP?
Metorial is MCP compatible and lets teams expose approved provider tools to MCP-capable agents and clients through a controlled access layer.How does Metorial control access to Gemini?
Metorial applies policies across users, groups, providers, agents, and individual tools, then records the context around every agent interaction.Can teams trace Gemini activity from agents?
Yes. Metorial records provider activity so teams can inspect tool calls, troubleshoot integrations, and give security teams the visibility they need.

