Search the web using Gemini's built-in Google Search.

Returns formatted search results (titles, URLs, snippets). Stateless utility.

Execute Python code using Gemini's built-in code execution sandbox.

Returns stdout, stderr, and any execution results. Stateless utility.

Consult Gemini for codebase analysis.

Gemini autonomously explores our project — reading files, listing directories, and searching code — so we don't need to pre-read files. Just describe what we need. Use file_paths only when specific files must be included.

Pipeline: For auto, flash, and pro, Lite explores quickly first, then our selected model synthesizes. "lite" and explicit model IDs skip the exploration phase and query directly. Gemini's tools: list_directory, read_file, search_project, git, gemini_search; FileSearch stores are searched automatically when present.

Stateless single-shot Gemini query with no session history.

Use for independent questions, one-off lookups, or batch-style queries where conversation context would be noise. Still has tool access (list_directory, read_file, etc.) and retry logic.

Upload a large file to Gemini's File API.

Create a Gemini context cache for a set of files.

Caching is useful for large files (>32k tokens) used across multiple turns. Model must be an explicit version (e.g., gemini-1.5-flash-001) or a supported alias.

Delete a Gemini context cache.

Create a new FileSearch store for semantic code search.

Upload files to the store with upload_to_file_store. Once populated, Gemini will automatically search the store during consult_gemini calls.

List all FileSearch stores with document counts and sizes.

Delete a FileSearch store and all its documents.

Upload a file to a FileSearch store for semantic search.

Supported: text, code, PDF, and other document formats. Files are chunked and embedded by Google for retrieval.

List available Gemini models grouped by capability.

Queries the Gemini API for all accessible models and groups them by supported actions (generateContent, generateImages, embedContent, etc.).

Generates an image using Imagen or Nano Banana (Gemini image) models.

Args: prompt: Text description of the image to generate. output_path: File path to save the generated image. model: Model alias or ID. "imagen" (default, ultra), "imagen-fast", "nano-pro", or "nano-flash". aspect_ratio: Aspect ratio (e.g. "1:1", "16:9", "9:16", "4:3", "3:4"). image_size: Output size for Nano Banana only (e.g. "1024x1024"). Not supported by Imagen.

Synthesizes speech from text.

Submit a batch of queries for async processing (~50% cost discount).

Batches run asynchronously (up to 24h). No tool use — inline all relevant context in each prompt. Use get_batch/list_batches to check status and get_batch_results when the job completes.

The system prompt follows the same configuration as consult_gemini (config.toml, --system-prompt CLI flags).

Args: queries: List of {custom_id: str, prompt: str} dicts. model: Model alias or ID (default: "flash"). "pro" enables deep thinking. temperature: Sampling temperature (default: 0.2).

Get the status of a batch job.

Args: name: Batch job name (e.g. "batches/abc") from create_batch.

List recent batch jobs.

Args: limit: Maximum number of batches to return (default: 20, max: 100).

Get results from a completed batch job.

Only available when state is JOB_STATE_SUCCEEDED or JOB_STATE_PARTIALLY_SUCCEEDED. Results include text extracted from each response, keyed by custom_id.

Args: name: Batch job name from create_batch.

Cancel a running batch job. Already-completed requests are unaffected.

Args: name: Batch job name from create_batch.

Delete a batch job. Only works on ended (succeeded/failed/cancelled) jobs.

Args: name: Batch job name from create_batch.