Retell AI 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 Retell AI 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 Retell AI. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Retell AI?"
)
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 Retell AI MCP Server
Connect your conversational assistant directly to Retell AI, a powerful platform for building voice-driven conversational agents. Empower your AI to orchestrate, analyze, and automate phone calls or web-based voice interactions seamlessly via simple text commands. From provisioning intelligent voice agents to placing outbound calls to customers, this integration brings the full telecommunication stack directly to your chat interface.
LlamaIndex agents combine Retell AI 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
- Automate Phone Calls — Command your assistant to initiate outbound voice interactions on your behalf (
create_phone_call) or register active sessions for web browser integration (register_web_call). - Build and Manage Voice Agents — Dynamically orchestrate AI agent personalities (
create_agent,update_agent) and configure their underlying conversational brain (create_llm) with specific system instructions and models. - Analyze Telemetry — Keep track of your infrastructure by querying historical call logs (
list_calls), investigating specific conversations for transcripts and sentiment analysis (get_call_details), surveying available text-to-speech voices (list_voices), and reviewing provisioned communication lines (list_phone_numbersandlist_agents).
The Retell AI 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 Retell AI to LlamaIndex via MCP
Follow these steps to integrate the Retell AI 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 Retell AI
Why Use LlamaIndex with the Retell AI MCP Server
LlamaIndex provides unique advantages when paired with Retell AI through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Retell AI tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Retell AI tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Retell AI, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Retell AI tools were called, what data was returned, and how it influenced the final answer
Retell AI + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Retell AI MCP Server delivers measurable value.
Hybrid search: combine Retell AI real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Retell AI 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 Retell AI for fresh data
Analytical workflows: chain Retell AI queries with LlamaIndex's data connectors to build multi-source analytical reports
Retell AI MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Retell AI to LlamaIndex via MCP:
create_agent
Creates a new AI voice agent
create_llm
Configures a Retell-hosted LLM
create_phone_call
Provide a JSON payload with "from_number" and "to_number". Initiates an outbound phone call
get_call_details
Retrieves details for a specific call
list_agents
Lists all configured AI voice agents
list_calls
Lists all historical and active calls
list_phone_numbers
Lists all phone numbers associated with the account
list_voices
Lists all available text-to-speech voices
register_web_call
Registers a new web-based call
update_agent
Updates an existing AI voice agent
Example Prompts for Retell AI in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Retell AI immediately.
"Can you show me the transcripts for call ID `c_f3a123`?"
"List all available agents I can use."
"We are testing out new numbers. Please use 'from_number' `+18005551234` and dial `+14085551234` assigning my 'agent_555'."
Troubleshooting Retell AI MCP Server with LlamaIndex
Common issues when connecting Retell AI to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpRetell AI + LlamaIndex FAQ
Common questions about integrating Retell AI 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 Retell AI 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 Retell AI to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.