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mcp_client_app.py

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+import asyncio
+import json
+
+import pandas as pd
+import streamlit as st
+from tensorus.mcp_client import TensorusMCPClient
+
+st.title("Tensorus MCP Client Demo")
+st.markdown("Interact with a Tensorus MCP server without writing any code.")
+
+mcp_url = st.text_input("MCP server URL", TensorusMCPClient.DEFAULT_MCP_URL)
+
+
+def run_async(coro):
+    return asyncio.run(coro)
+
+
+st.header("Datasets")
+if st.button("List datasets"):
+
+    async def _list():
+        async with TensorusMCPClient.from_http(url=mcp_url) as client:
+            return await client.list_datasets()
+
+    result = run_async(_list())
+    if result:
+        st.write(pd.DataFrame(result.datasets, columns=["Datasets"]))
+
+create_name = st.text_input("New dataset name")
+if st.button("Create dataset") and create_name:
+
+    async def _create():
+        async with TensorusMCPClient.from_http(url=mcp_url) as client:
+            return await client.create_dataset(create_name)
+
+    res = run_async(_create())
+    if res:
+        st.success(res.message or "Dataset created")
+
+st.header("Ingest Tensor")
+with st.form("ingest"):
+    ingest_ds = st.text_input("Dataset", key="ingest_ds")
+    tensor_shape = st.text_input("Tensor shape", value="2,2")
+    tensor_dtype = st.text_input("Tensor dtype", value="float32")
+    tensor_data = st.text_area("Tensor data (JSON)", value="[[0, 0], [1, 1]]")
+    metadata = st.text_area("Metadata (JSON)", value="{}")
+    submitted = st.form_submit_button("Ingest")
+
+if submitted:
+    try:
+        shape = [int(x) for x in tensor_shape.split(",") if x.strip()]
+        data = json.loads(tensor_data)
+        meta = json.loads(metadata) if metadata.strip() else None
+
+        async def _ingest():
+            async with TensorusMCPClient.from_http(url=mcp_url) as client:
+                return await client.ingest_tensor(
+                    dataset_name=ingest_ds,
+                    tensor_shape=shape,
+                    tensor_dtype=tensor_dtype,
+                    tensor_data=data,
+                    metadata=meta,
+                )
+
+        response = run_async(_ingest())
+        st.write(response)
+        st.success(f"Ingested tensor {response.id}")
+    except Exception as e:
+        st.error(f"Failed to ingest: {e}")
+
+st.header("Run NQL Query")
+query = st.text_input("Query", key="nql_query")
+if st.button("Execute") and query:
+
+    async def _query():
+        async with TensorusMCPClient.from_http(url=mcp_url) as client:
+            return await client.execute_nql_query(query)
+
+    result = run_async(_query())
+    if isinstance(result.results, list):
+        st.write(pd.DataFrame(result.results))
+    else:
+        st.json(result.results)

mcp_client_demo.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tensorus MCP Client Demo\n",
+    "\n",
+    "This notebook demonstrates basic usage of `TensorusMCPClient` for creating datasets, ingesting tensors, and running simple queries against the default MCP server."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "from tensorus.mcp_client import TensorusMCPClient"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "import asyncio\n",
+    "\n",
+    "async def demo():\n",
+    "    async with TensorusMCPClient.from_http() as client:\n",
+    "        create_resp = await client.create_dataset(\"demo_ds\")\n",
+    "        print(\"Create dataset:\", create_resp)\n",
+    "\n",
+    "        ingest_resp = await client.ingest_tensor(\n",
+    "            dataset_name=\"demo_ds\",\n",
+    "            tensor_shape=[2, 2],\n",
+    "            tensor_dtype=\"float32\",\n",
+    "            tensor_data=[[1.0, 2.0], [3.0, 4.0]],\n",
+    "            metadata={\"source\": \"demo\"}\n",
+    "        )\n",
+    "        print(\"Ingest tensor:\", ingest_resp)\n",
+    "\n",
+    "        datasets = await client.list_datasets()\n",
+    "        print(\"Datasets:\", datasets.datasets)\n",
+    "\n",
+    "        details = await client.get_tensor_details(\"demo_ds\", ingest_resp.id)\n",
+    "        print(\"Tensor details:\", details)\n",
+    "\n",
+    "        count = await client.execute_nql_query(\"count\")\n",
+    "        print(\"NQL count result:\", count.results)\n",
+    "\n",
+    "await demo()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "pygments_lexer": "ipython3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

mcp_client_requirements.txt

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+# Minimal requirements for demo/mcp_client_app.py
+fastmcp>=0.2.0
+streamlit>=1.25.0
+pandas>=1.5.0