diff --git a/README.md b/README.md
index 6717d68e..3322e532 100644
--- a/README.md
+++ b/README.md
@@ -24,41 +24,33 @@ This MCP is **not** just documentation or static code analysis. It is a **semant
 
 3. **Minimal MCP Surface** - Exposes only what an LLM needs: Discovery, Description, Instantiation, Execution, and model persistence.
 
-## 🛠️ Prerequisites
-
-- **Python 3.10+**
-- **pip** package manager
-
 ## 🛠️ Installation
 
-### Virtual Environment Setup
+### Zero-install via uvx (recommended)
 
-It is recommended to use a virtual environment:
+If you have [uv](https://github.com/astral-sh/uv) installed, no separate installation step is needed. Just update your MCP client config (see [Connecting from an LLM Client](#connecting-from-an-llm-client) below) and `uvx` will handle the rest automatically.
 
 ```bash
-python3 -m venv .venv
-source .venv/bin/activate  # On Windows: .venv\Scripts\activate
+# Verify uv is available
+uvx sktime-mcp --help
 ```
 
-### Package Installation
-
-Install from PyPI (recommended for users):
+### pip
 
 ```bash
 pip install sktime-mcp
 
-# With all optional dependencies
+# With optional extras (SQL, forecasting models, file formats)
 pip install "sktime-mcp[all]"
 ```
 
-For development (editable install from source):
+### Development installation
 
 ```bash
-pip install -e ".[dev]"
+git clone https://github.com/sktime/sktime-mcp
+cd sktime-mcp
+python3 -m pip install -e ".[dev]"
 ```
-
-For a more detailed first-time setup flow, including MCP server verification and troubleshooting, see [Beginner Setup](#-beginner-setup-firsttime-users).
-
 ## 🧭 Beginner Setup (First‑Time Users)
 
 If you are new to sktime‑mcp or to MCP‑based workflows, this section provides a minimal starting point to help you verify that your setup is working correctly.
@@ -140,24 +132,36 @@ The server uses stdio transport by default, compatible with Claude Desktop, Clau
 | Linux    | `~/.config/claude/claude_desktop_config.json` |
 | Windows  | `%APPDATA%\Claude\claude_desktop_config.json` |
 
+**With uvx (recommended — no prior install needed):**
 ```json
 {
   "mcpServers": {
     "sktime": {
-      "command": "sktime-mcp"
+      "command": "uvx",
+      "args": ["sktime-mcp"]
     }
   }
 }
 ```
 
-If you are using a virtual environment, or if `sktime-mcp` is not on your `PATH`, point the client directly at the environment's Python executable — this ensures the correct packages are used:
+**With optional extras:**
+```json
+{
+  "mcpServers": {
+    "sktime": {
+      "command": "uvx",
+      "args": ["sktime-mcp[forecasting,sql]"]
+    }
+  }
+}
+```
 
+**With pip-installed package:**
 ```json
 {
   "mcpServers": {
     "sktime": {
-      "command": "<project-root>/.venv/bin/python",
-      "args": ["-m", "sktime_mcp.server"]
+      "command": "sktime-mcp"
     }
   }
 }
diff --git a/docs/source/intro.md b/docs/source/intro.md
index 64b4408c..c86662a5 100644
--- a/docs/source/intro.md
+++ b/docs/source/intro.md
@@ -46,12 +46,14 @@ Get up and running in seconds. Use with **Claude Desktop**, **Cursor**, or any M
 
 ### 1. Install
 
+**Zero-install via uvx (recommended):** if you have [uv](https://github.com/astral-sh/uv) installed, skip this step — uvx fetches and runs the package automatically when your MCP client starts.
+
 ```bash
-# Install directly from PyPI (with all optional dependencies recommended)
-pip install "sktime-mcp[all]"
+# Or install explicitly with pip
+pip install sktime-mcp
 ```
 
-Alternatively, when contributing, use GitHub to install from source:
+When contributing, install from source:
 
 ```bash
 git clone https://github.com/sktime/sktime-mcp.git
@@ -61,13 +63,22 @@ source .venv/bin/activate
 pip install -e ".[dev]"
 ```
 
-### 2. Run
-```bash
-sktime-mcp
+### 2. Connect (Claude Desktop / Claude Code Config)
+Add this to your `claude_desktop_config.json`:
+
+**With uvx (no prior install needed):**
+```json
+{
+  "mcpServers": {
+    "sktime": {
+      "command": "uvx",
+      "args": ["sktime-mcp"]
+    }
+  }
+}
 ```
 
-### 3. Connect (Claude Desktop Config)
-Add this to your `claude_desktop_config.json`:
+**With pip-installed package:**
 ```json
 {
   "mcpServers": {
diff --git a/docs/source/user-guide.md b/docs/source/user-guide.md
index 36a0eb04..80a5be36 100644
--- a/docs/source/user-guide.md
+++ b/docs/source/user-guide.md
@@ -11,31 +11,63 @@ Welcome to the **sktime-mcp** User Guide. This guide walks you through installin
 Before you begin, ensure you have:
 
 - **Python 3.10+** installed.
-- **pip** package manager.
-- A compatible MCP client (like **Claude Desktop**, **Cursor**, or compatible **VS Code extensions** like Cline).
+- A compatible MCP client (like **Claude Desktop**, **Cursor**, or **VS Code with Copilot**).
 
 ### Installation
 
-Install the package directly from the source. We recommend installing with all dependencies to unlock full functionality.
+**Zero-install via uvx (recommended):** if you have [uv](https://github.com/astral-sh/uv) installed, no install step is needed. Just configure your MCP client (see below) and `uvx` handles everything automatically.
 
 ```bash
-# Standard installation
-pip install -e .
+# Or install with pip
+pip install sktime-mcp
 
-# Recommended: Install with all optional extras (SQL, Forecasting, Files)
-pip install -e ".[all]"
+# With all optional extras (SQL, forecasting models, file formats)
+pip install "sktime-mcp[all]"
 ```
 
-### Running the Server
+### MCP Client Configuration
 
-Start the MCP server to begin listening for connections:
+**With uvx (recommended — no prior install needed):**
+```json
+{
+  "mcpServers": {
+    "sktime": {
+      "command": "uvx",
+      "args": ["sktime-mcp"]
+    }
+  }
+}
+```
 
-```bash
-sktime-mcp
+**With optional extras:**
+```json
+{
+  "mcpServers": {
+    "sktime": {
+      "command": "uvx",
+      "args": ["sktime-mcp[forecasting,sql]"]
+    }
+  }
+}
 ```
 
-*Or manually via Python:*
+**With pip-installed package:**
+```json
+{
+  "mcpServers": {
+    "sktime": {
+      "command": "sktime-mcp"
+    }
+  }
+}
+```
+
+### Running the Server manually
+
 ```bash
+sktime-mcp
+
+# Or via Python
 python -m sktime_mcp.server
 ```
 
diff --git a/src/sktime_mcp/__main__.py b/src/sktime_mcp/__main__.py
new file mode 100644
index 00000000..b82e74f0
--- /dev/null
+++ b/src/sktime_mcp/__main__.py
@@ -0,0 +1,5 @@
+"""Allow `python -m sktime_mcp` as an alternative entry point."""
+
+from sktime_mcp.server import main
+
+main()
diff --git a/src/sktime_mcp/runtime/executor.py b/src/sktime_mcp/runtime/executor.py
index 0dc2673f..094c63b9 100644
--- a/src/sktime_mcp/runtime/executor.py
+++ b/src/sktime_mcp/runtime/executor.py
@@ -21,7 +21,7 @@
 logger = logging.getLogger(__name__)
 
 
-# Dynamically discover all available sktime demo datasets at import time.
+# Dynamically discover all available sktime demo datasets on first access.
 # This replaces the old hardcoded dictionary and automatically exposes every
 # load_* function in sktime.datasets to the MCP server.
 def _discover_demo_datasets() -> dict:
@@ -39,7 +39,15 @@ def _discover_demo_datasets() -> dict:
         return {}  # fallback: empty dict if sktime not installed
 
 
-DEMO_DATASETS = _discover_demo_datasets()
+_DEMO_DATASETS: dict | None = None
+
+
+def _get_demo_datasets() -> dict:
+    """Lazy singleton — discovers datasets only on first call."""
+    global _DEMO_DATASETS
+    if _DEMO_DATASETS is None:
+        _DEMO_DATASETS = _discover_demo_datasets()
+    return _DEMO_DATASETS
 
 
 class Executor:
@@ -87,15 +95,16 @@ def instantiate(
     # L-7: We can also add custom load_dataset functions here
     def load_dataset(self, name: str) -> dict[str, Any]:
         """Load a demo dataset."""
-        if name not in DEMO_DATASETS:
+        demo_datasets = _get_demo_datasets()
+        if name not in demo_datasets:
             return {
                 "success": False,
                 "error": f"Unknown dataset: {name}",
-                "available": list(DEMO_DATASETS.keys()),
+                "available": list(demo_datasets.keys()),
             }
 
         try:
-            module_path = DEMO_DATASETS[name]
+            module_path = demo_datasets[name]
             parts = module_path.rsplit(".", 1)
             module = __import__(parts[0], fromlist=[parts[1]])
             loader = getattr(module, parts[1])
@@ -529,7 +538,7 @@ def instantiate_pipeline(
 
     def list_datasets(self) -> list[str]:
         """List available demo datasets."""
-        return list(DEMO_DATASETS.keys())
+        return list(_get_demo_datasets().keys())
 
     def load_data_source(self, config: dict[str, Any]) -> dict[str, Any]:
         """
diff --git a/src/sktime_mcp/tools/codegen.py b/src/sktime_mcp/tools/codegen.py
index e68a6df6..a833903e 100644
--- a/src/sktime_mcp/tools/codegen.py
+++ b/src/sktime_mcp/tools/codegen.py
@@ -8,7 +8,7 @@
 from typing import Any
 
 from sktime_mcp.registry.interface import get_registry
-from sktime_mcp.runtime.executor import DEMO_DATASETS
+from sktime_mcp.runtime.executor import _get_demo_datasets
 from sktime_mcp.runtime.handles import get_handle_manager
 
 
@@ -253,9 +253,10 @@ def export_code_tool(
 
     # Optionally add fit/predict example
     if include_fit_example:
-        # Resolve the dataset loader from DEMO_DATASETS
-        if dataset and dataset in DEMO_DATASETS:
-            module_path = DEMO_DATASETS[dataset]
+        # Resolve the dataset loader from demo datasets
+        _demo_datasets = _get_demo_datasets()
+        if dataset and dataset in _demo_datasets:
+            module_path = _demo_datasets[dataset]
             module_parts = module_path.rsplit(".", 1)
             loader_module = module_parts[0]
             loader_func = module_parts[1]