Connect Claude to Google Gemini

List the Gemini models this API key can call, each with its id, display name, description, input and output token limits, and the list of methods it supports - generateContent, countTokens, embedContent and so on. Call this before generate_content or embed_content whenever a model id is not certain: this connector deliberately hard-codes no model names, because Google's roster changes faster than a file in a repository can. Note that ids come back in "models/gemini-…" form, while every operation here takes the bare id without the "models/" prefix. Free, and the cheapest proof that the key works.

Read one model by id: display name, description, input and output token limits, default sampling settings, and which generation methods it supports. Use it to confirm a model can do what a request needs - that it lists embedContent before calling embed_content, or that its inputTokenLimit is large enough for a prompt count_tokens just measured. A 404 means this key cannot reach that id; call list_models to see what it can.

Count how many input tokens a prompt would use against a given model, without generating anything and without being billed. A read, because nothing is created and nothing is charged. Use it before generate_content to check a prompt fits the model's inputTokenLimit, which get_model reports. Takes the same `contents` that generate_content takes, so count exactly what you intend to send. It measures input only; output tokens, usually the larger cost, are not predicted.

Turn one piece of text into an embedding vector, for similarity search, clustering or retrieval. A read: it creates nothing and stores nothing, it only measures. Use an embedding model, not a generation model - "gemini-embedding-001" is the current one, and list_models shows which ids list embedContent among their supported methods. Set task_type to match what the vector is for; Gemini tunes the embedding to it, and using RETRIEVAL_QUERY for a search query against a corpus embedded with RETRIEVAL_DOCUMENT is what makes retrieval work. output_dimensionality shortens the vector when storage matters. For many strings at once, call batch_embed_contents instead.

Embed several strings in one request, returning one vector per string in the order given. Prefer this over calling embed_content repeatedly - Gemini counts each request against a per-minute rate limit, so one batch of fifty strings costs one request instead of fifty. Same model, task_type and dimensionality rules as embed_content, applied to every string in the batch. A read: nothing is created or stored.

List the files uploaded to this API key's Gemini File API storage: each one's name, display name, MIME type, size, state, expiry, and the `uri` that a generate_content part refers to as {"fileData":{"mimeType":…,"fileUri":…}}. That URI is the reason to call this. Gemini deletes uploaded files automatically after 48 hours, so a file listed yesterday may already be gone. Uploading is not offered here - it needs Google's resumable multipart upload, which this connector does not carry - so this lists what was put there by other means.

Read one uploaded file's record: display name, MIME type, size, expiry, and its `state`. State is what this is usually called for - a freshly uploaded video sits at PROCESSING and a generate_content call referring to it fails until it reaches ACTIVE. Returns metadata only; the bytes are not downloadable through this API. Get the id from list_files.