Does Ollama keep my data local?

Yes. Ollama runs open models entirely on your own machine, so prompts and data do not leave the host. The Gemini server, by contrast, sends requests to Google's hosted Gemini API.

Can the Gemini server manage model weights?

No. It calls Google's hosted API and can list available models, but it does not manage weights. The Ollama server can pull, push, create, copy, remove, list, and show local models.

Google Gemini vs Ollama

Both of these maintained community MCP servers let an agent hand work off to a language model, but one calls a hosted frontier API and the other drives models running entirely on your own machine. The Gemini server (mcp-server-gemini) wraps Google's Gemini API behind a small set of focused tools over stdio: generate text, analyze an image, count tokens before a large request, list available models, and create embeddings. The Ollama server (the maintained ollama-mcp-server package) gives an agent full control of a local Ollama runtime, where open models like Llama, Mistral, Gemma, Qwen, and DeepSeek run with no data leaving the host: pull, push, run, and chat with models, create, copy, remove, list, and show them, and call an OpenAI-compatible chat completion. So the choice is between Google's cloud Gemini models and a private, local model runtime — a difference in privacy, cost model, and which models you can reach. Here is how they compare for an agent.

How they compare

Dimension	Google Gemini	Ollama
Where models run	In Google's cloud via the Gemini API — frontier multimodal models, called remotely.	On your own machine via Ollama — open models run locally with no data leaving the host.
Privacy and data	Requests go to Google's API; suitable when cloud processing is acceptable.	Fully local, so prompts and data stay on the host — strong fit for private or offline use.
Model management	List available models; you do not manage weights, just call the hosted API.	Full lifecycle: pull, push, create, copy, remove, list, and show models on the local runtime.
Capabilities	Generate text, analyze images, count tokens, and create embeddings against Gemini.	Run and chat with local LLMs and call an OpenAI-compatible chat completion.
Best-fit task	Delegating multimodal generation and embeddings to Google's hosted Gemini models.	Running and managing open models privately on your own hardware, online or offline.

Verdict

Choose by where you want inference to happen. The Gemini server is the pick when cloud processing is fine and you want Google's frontier multimodal models — text generation, image analysis, token counting, and embeddings — behind a small, focused tool set. The Ollama server is the choice when privacy, cost control, or offline operation matters: it drives a local runtime where open models like Llama, Mistral, and Qwen run with no data leaving the host, and it gives the agent full model lifecycle control plus an OpenAI-compatible chat API. Both are maintained community servers, so the decision is hosted-Gemini versus local-Ollama. Match the server to your privacy posture, cost model, and the models you want the agent to reach.

FAQ

Does Ollama keep my data local?: Yes. Ollama runs open models entirely on your own machine, so prompts and data do not leave the host. The Gemini server, by contrast, sends requests to Google's hosted Gemini API.
Can the Gemini server manage model weights?: No. It calls Google's hosted API and can list available models, but it does not manage weights. The Ollama server can pull, push, create, copy, remove, list, and show local models.