SenseCore Platform MCP Server for LlamaIndex 11 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add SenseCore Platform as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to SenseCore Platform. "
"You have 11 tools available."
),
)
response = await agent.run(
"What tools are available in SenseCore Platform?"
)
print(response)
asyncio.run(main())
* 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.
LlamaIndex agents combine SenseCore Platform tool responses with indexed documents for comprehensive, grounded answers. Connect 11 tools through the Vinkius and query live data alongside vector stores and SQL databases in a single turn — ideal for hybrid search, data enrichment, and analytical workflows.
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 LlamaIndex 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 LlamaIndex via MCP
Follow these steps to integrate the SenseCore Platform MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 11 tools from SenseCore Platform
Why Use LlamaIndex with the SenseCore Platform MCP Server
LlamaIndex provides unique advantages when paired with SenseCore Platform through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine SenseCore Platform tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain SenseCore Platform tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query SenseCore Platform, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what SenseCore Platform tools were called, what data was returned, and how it influenced the final answer
SenseCore Platform + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the SenseCore Platform MCP Server delivers measurable value.
Hybrid search: combine SenseCore Platform real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query SenseCore Platform to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying SenseCore Platform for fresh data
Analytical workflows: chain SenseCore Platform queries with LlamaIndex's data connectors to build multi-source analytical reports
SenseCore Platform MCP Tools for LlamaIndex (11)
These 11 tools become available when you connect SenseCore Platform to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting SenseCore Platform to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpSenseCore Platform + LlamaIndex FAQ
Common questions about integrating SenseCore Platform MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect SenseCore Platform with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect SenseCore Platform to LlamaIndex
Get your token, paste the configuration, and start using 11 tools in under 2 minutes. No API key management needed.