Can I automatically list all available models in my SenseCore project?

Yes! Use the `list_models` tool. Your agent will retrieve a complete list of all SenseTime foundation models and specialized variants currently active in your account.

How do I check the health status of my deployed model services?

Use the `get_service_health` tool with the specific Service ID. The agent will return real-time metrics on availability, throughput, and average latency.

Can I monitor GPU resource utilization via the AI agent?

Yes! The `get_resource_usage` tool retrieves granular metrics on compute node utilization and remaining quota for your specific project environment.

SenseCore Platform MCP Server for LangChain 11 tools — connect in under 2 minutes

Name: SenseCore Platform
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 11 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect SenseCore Platform through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "sensecore-platform": {
            "transport": "streamable_http",
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
        }
    }) as client:
        tools = client.get_tools()
        agent = create_react_agent(
            ChatOpenAI(model="gpt-4o"),
            tools,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using SenseCore Platform, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

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IAMAccess control

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DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About SenseCore Platform MCP Server

Connect your AI agents to the SenseCore Platform, the industrial-grade AI infrastructure by SenseTime. This MCP provides 10 tools to manage advanced foundation models, orchestrate large-scale chat completions, and monitor high-performance compute resources programmatically.

LangChain's ecosystem of 500+ components combines seamlessly with SenseCore Platform through native MCP adapters. Connect 11 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

What you can do

SenseChat Interaction — Trigger chat completions with SenseTime's foundation models using persistent context and history
Model Intelligence — List all available foundation models and retrieve granular technical specifications for each version
Resource Management — Monitor compute node availability and track quota consumption across your organizational projects
Service Monitoring — Check real-time health and latency metrics for deployed model services
Async Operations — List and track the status of long-running training or inference tasks on the SenseCore infrastructure

The SenseCore Platform MCP Server exposes 11 tools through the Vinkius. Connect it to LangChain in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect SenseCore Platform to LangChain via MCP

Follow these steps to integrate the SenseCore Platform MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 11 tools from SenseCore Platform via MCP

Why Use LangChain with the SenseCore Platform MCP Server

LangChain provides unique advantages when paired with SenseCore Platform through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents. combine SenseCore Platform MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across SenseCore Platform queries for multi-turn workflows

SenseCore Platform + LangChain Use Cases

Practical scenarios where LangChain combined with the SenseCore Platform MCP Server delivers measurable value.

RAG with live data: combine SenseCore Platform tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query SenseCore Platform, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain SenseCore Platform tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every SenseCore Platform tool call, measure latency, and optimize your agent's performance

SenseCore Platform MCP Tools for LangChain (11)

These 11 tools become available when you connect SenseCore Platform to LangChain via MCP:

chat_completions

Send a message to a SenseCore large language model

create_assistant

Define a new AI assistant

create_message

Add a message to a thread

create_run

Execute an assistant on a thread

create_thread

Initialize a new conversation thread

get_assistant_details

Get complete configuration for an assistant

get_run_status

Check the status of an active assistant run

list_assistants

List all configured assistants

list_files

List uploaded files

list_messages

Retrieve the message history of a thread

list_models

List all available SenseNova models

Example Prompts for SenseCore Platform in LangChain

Ready-to-use prompts you can give your LangChain agent to start working with SenseCore Platform immediately.

"Chat with SenseChat-5 and ask 'Compare the features of traditional neural networks and transformers'."

"List all active models in project 'Research_AI_2024'."

"What is the health status of service ID 'svc_gpu_999'?"

Troubleshooting SenseCore Platform MCP Server with LangChain

Common issues when connecting SenseCore Platform to LangChain through the Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

SenseCore Platform + LangChain FAQ

Common questions about integrating SenseCore Platform MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.