Mio MCP Server for LlamaIndexGive LlamaIndex instant access to 12 tools to Create Webhook, Delete Webhook, Get Account Info, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Mio 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 App Connector for LlamaIndex
The Mio app connector for LlamaIndex is a standout in the Communication Messaging category — giving your AI agent 12 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
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 Mio. "
"You have 12 tools available."
),
)
response = await agent.run(
"What tools are available in Mio?"
)
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 Mio MCP Server
Connect your Mio account to any AI agent and manage automated phone calls through natural conversation.
LlamaIndex agents combine Mio tool responses with indexed documents for comprehensive, grounded answers. Connect 12 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
- Outbound Calls — Start AI-powered phone calls with custom scripts and instructions
- Call Logs — Browse call history with status, duration, and outcomes
- Transcripts — Retrieve full text transcriptions of completed calls
- AI Summaries — Get AI-generated summaries and extracted data from calls
- Voice Selection — Choose from multiple neural voices for the AI agent
- Webhooks — Configure event notifications for call status changes
- Call Control — Terminate active calls in real time
- Account — Check credit balance and account information
The Mio MCP Server exposes 12 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.
All 12 Mio tools available for LlamaIndex
When LlamaIndex connects to Mio through Vinkius, your AI agent gets direct access to every tool listed below — spanning interoperability, outbound-calling, call-automation, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Add new notification
Remove a webhook
Get user profile
Get specific call info
Get AI call summary
Get call text log
Check account funds
List AI voices
List all call logs
Get active webhooks
Start an AI phone call
Stop active call
Connect Mio to LlamaIndex via MCP
Follow these steps to wire Mio into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the Mio MCP Server
LlamaIndex provides unique advantages when paired with Mio through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Mio tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Mio tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Mio, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Mio tools were called, what data was returned, and how it influenced the final answer
Mio + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Mio MCP Server delivers measurable value.
Hybrid search: combine Mio real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Mio 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 Mio for fresh data
Analytical workflows: chain Mio queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for Mio in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Mio immediately.
"Start an AI call to confirm tomorrow's appointment with Sarah."
"Get the transcript and summary for call_890."
"Show available AI voices and my credit balance."
Troubleshooting Mio MCP Server with LlamaIndex
Common issues when connecting Mio to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpMio + LlamaIndex FAQ
Common questions about integrating Mio MCP Server with LlamaIndex.