DingConnect MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add DingConnect as an MCP tool provider through 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 DingConnect. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in DingConnect?"
)
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 DingConnect MCP Server
Integrate DingConnect, the world's leading mobile top-up platform, directly into your AI workflow. Access thousands of mobile operators globally, manage your top-up and data products, monitor real-time account balances, and track transaction history using natural language.
LlamaIndex agents combine DingConnect tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through 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
- Operator Discovery — List and search for mobile network operators across 150+ countries supported by DingConnect.
- Product Intelligence — Access detailed information on available top-up and data plans, including technical keys and pricing.
- Transaction Auditing — List and retrieve detailed history for past service executions and their status.
- Balance Management — Track your account credit balance and organizational limits directly via chat.
The DingConnect MCP Server exposes 10 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 DingConnect to LlamaIndex via MCP
Follow these steps to integrate the DingConnect 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 10 tools from DingConnect
Why Use LlamaIndex with the DingConnect MCP Server
LlamaIndex provides unique advantages when paired with DingConnect through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine DingConnect tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain DingConnect tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query DingConnect, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what DingConnect tools were called, what data was returned, and how it influenced the final answer
DingConnect + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the DingConnect MCP Server delivers measurable value.
Hybrid search: combine DingConnect real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query DingConnect 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 DingConnect for fresh data
Analytical workflows: chain DingConnect queries with LlamaIndex's data connectors to build multi-source analytical reports
DingConnect MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect DingConnect to LlamaIndex via MCP:
check_mobile_service_status
Check if mobile services are available for a specific destination (mock logic)
get_account_credit_balance
Retrieve the current credit balance for your DingConnect account
get_api_account_metadata
Retrieve metadata and settings for your DingConnect API account
list_available_topup_products
List all available top-up and data products for a specific provider
list_mobile_operators
List all mobile network operators (providers) for a specific country
list_supported_countries
List all countries supported by DingConnect for mobile services
list_top_volume_countries
Identify countries with high service availability (mock logic)
list_transaction_history
List recent top-up transactions and service history
quick_operator_audit
Retrieve a high-level summary of operators and products for a country
search_topup_products
Search for specific top-up or data products by name keyword
Example Prompts for DingConnect in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with DingConnect immediately.
"List all mobile operators available in Brazil."
"What is my current account credit balance?"
"Show me the top-up plans for operator 'Safaricom' in Kenya."
Troubleshooting DingConnect MCP Server with LlamaIndex
Common issues when connecting DingConnect to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpDingConnect + LlamaIndex FAQ
Common questions about integrating DingConnect 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 DingConnect 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 DingConnect to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.