How to Use the Mio MCP in LlamaIndex

Q: How does LlamaIndex convert Mio call transcripts into searchable vector data?

The framework pulls the text using getcalltranscript and splits it into document nodes. These nodes are then embedded and stored in your vector database, allowing semantic search over your entire voice call history.

Q: How do I configure Mio webhook events to trigger LlamaIndex indexing?

You use createwebhook to register an endpoint that listens for completed calls. When a call ends, the webhook payload triggers your LlamaIndex pipeline to pull the transcript and update your vector store automatically.

Q: What tools are available to check my current platform status?

You can use getaccountinfo to retrieve your profile details and getcreditbalance to monitor your current wallet status, ensuring your automated indexing tasks never run out of API credits.

Index Mio voice transcripts directly into LlamaIndex vector stores to build searchable knowledge bases from live phone calls.

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Connect Mio MCP to LlamaIndex

Create your Vinkius account to connect Mio to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Mio with LlamaIndex

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LlamaIndex works by turning your Mio voice data into searchable indexes. When you connect this server, you can fetch voice records using `list_calls` and feed the raw text from `get_call_transcript` straight into your LlamaIndex vector database. Your LlamaIndex agents can query past Mio calls semantic-style instead of hunting through audio files. If a user asks what was agreed upon last Tuesday, LlamaIndex searches the indexed output of `get_call_summary` and provides an answer grounded in actual conversation history.

Feed live Mio call metadata into LlamaIndex documents.

You need more than just raw text to build a reliable LlamaIndex search index. By calling `get_call_details`, LlamaIndex grabs critical metadata like call duration, timestamps, and caller IDs to attach as attributes to your document chunks. This metadata makes your LlamaIndex search queries highly precise. You can filter your vector search to only look at Mio transcripts generated by specific voices from `list_available_voices` or restrict the search window to calls made within the last week.

Monitor Mio system health inside LlamaIndex.

LlamaIndex agents can track their own operating parameters by querying this Mio MCP server. The agent can check `get_account_info` and `get_credit_balance` to ensure there are enough funds before attempting to index new Mio voice sessions. If the LlamaIndex system detects a low balance, it can trigger alert workflows. You can also clean up dead data feeds by calling `list_webhooks` and removing obsolete listeners using `delete_webhook` to keep your LlamaIndex indexing pipelines clean.

Setup guide

Set up Mio MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Mio MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Mio tools.",
)
response = await agent.run("List recent Mio data")

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Common questions about Mio MCP in LlamaIndex

The framework pulls the text using `get_call_transcript` and splits it into document nodes. These nodes are then embedded and stored in your vector database, allowing semantic search over your entire voice call history.

Yes. If a query reveals a critical issue that requires human intervention, your LlamaIndex agent can invoke `start_ai_call` to initiate a voice session, using parameters retrieved from your indexed documents.

You use `create_webhook` to register an endpoint that listens for completed calls. When a call ends, the webhook payload triggers your LlamaIndex pipeline to pull the transcript and update your vector store automatically.

You can use `get_account_info` to retrieve your profile details and `get_credit_balance` to monitor your current wallet status, ensuring your automated indexing tasks never run out of API credits.

Processing happens entirely within sandboxed V8 environments that do not persist data. Your voice calls, transcripts, and webhook configurations are handled with strict memory isolation, meaning no sensitive communication data is written to permanent disk storage on our platform.

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