Ollama Cloud MCP Server

An MCP server with a ChatGPT-style web interface for choosing and chatting with Ollama Cloud models. The backend talks directly to https://ollama.com/api; it does not require a local Ollama daemon or local model downloads.

What is included

• server/mcp_server.py - MCP stdio server with 12 tools, three prompts, and an ollama://models resource.

• server/web_tools.py - keyless public web search and readable webpage extraction.

• server/api.py - HTTP bridge used by the browser, including NDJSON chat streaming.

• frontend/ - responsive React/Vite ChatGPT-style interface.

• setup.ps1, run-backend.ps1, run-frontend.ps1 - PowerShell launchers.

Related MCP server: Local-MCP-server

Quick start

Create or update the ignored project .env with your Ollama API key:

OLLAMA_API_KEY=your_ollama_api_key
OLLAMA_BASE_URL=https://ollama.com
OLLAMA_DEFAULT_MODEL=gpt-oss:120b-cloud
OLLAMA_REQUEST_TIMEOUT=300

The existing .env in this checkout already contains a configured key; do not commit or print it. Install dependencies once:

cd 'C:\Users\admin\Desktop\New folder\MCP_SERVER'
.\setup.ps1

Start the backend and frontend in separate PowerShell terminals:

# terminal 1
cd 'C:\Users\admin\Desktop\New folder\MCP_SERVER'
.\run-backend.ps1

# terminal 2
cd 'C:\Users\admin\Desktop\New folder\MCP_SERVER'
.\run-frontend.ps1

Open http://127.0.0.1:5173. The model selector is populated from the authenticated Ollama Cloud API, so available hosted models can be chosen without changing code.

The direct Cloud API uses the base model name when sending requests. For example, the UI alias gpt-oss:120b-cloud is sent to Ollama Cloud as gpt-oss:120b.

MCP client configuration

For an MCP client that supports stdio servers, use:

{
  "mcpServers": {
    "ollama-cloud": {
      "command": "C:\\Users\\admin\\Desktop\\New folder\\MCP_SERVER\\.venv\\Scripts\\python.exe",
      "args": ["-m", "server.mcp_server"],
      "cwd": "C:\\Users\\admin\\Desktop\\New folder\\MCP_SERVER"
    }
  }
}

Available MCP tools are list_ollama_models, chat_with_ollama, generate_with_ollama, embed_text, inspect_ollama_model, web_search, scrape_url, calculate, convert, current_time, text_stats, and pretty_print_json.

Available user-invoked MCP prompts are research_topic, solve_math_problem, and summarize_webpage. The server also exposes the read-only ollama://models resource with JSON metadata.

web_search uses DuckDuckGo's HTML endpoint and scrape_url fetches public HTML pages. Both require internet access; local/private network addresses are blocked by the scraper.

The general-purpose tools are local and deterministic: calculate supports safe arithmetic, convert supports length/mass/volume/temperature, current_time supports IANA timezones, text_stats counts text structure, and pretty_print_json validates and formats JSON.

The browser chat binds the 10 safe helper tools from server/tool_registry.py to Ollama Cloud's tool-calling API. When a compatible model requests a calculation, web search, scrape, conversion, or other helper, the backend executes it and sends the result back to the model before returning the final answer. The two model-to-model helpers (chat_with_ollama and generate_with_ollama) remain MCP-only to prevent recursive calls.