How to Use the Mav MCP in LlamaIndex

Q: Can LlamaIndex agents update Mav records?

Yes, once the agent decides on an action, it calls updatelead or stopplaybook. The changes are reflected in your Mav account immediately.

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Connect Mav MCP to LlamaIndex

Create your Vinkius account to connect Mav 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 Mav with LlamaIndex

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Index Mav lead data into LlamaIndex

You can ingest the results of `list_leads` and `list_playbooks` directly into your vector store. This gives your agent a searchable memory of every interaction. Instead of just calling an API, your agent answers questions about lead status by querying the index. It knows exactly which leads are in which playbook.

Ground SMS outreach in LlamaIndex facts

Your agent uses retrieved knowledge to make better decisions about which playbooks to trigger. It checks past activities via `list_activities` before sending a new text. This prevents the agent from repeating messages. It ensures every `create_lead` action is informed by the history of the conversation stored in your index.

Unified querying for Mav and LlamaIndex

Combine live API data with your existing documents. When you need to qualify a prospect, the agent searches your internal docs and the Mav lead record simultaneously. This creates a context-aware sales assistant that understands your business. It uses `get_lead` to pull live data while referencing your company playbooks for tone.

Setup guide

Set up Mav 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 Mav 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 Mav tools.",
)
response = await agent.run("List recent Mav data")

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Common questions about Mav MCP in LlamaIndex

You use the MCP client to fetch lead lists and activities. Once retrieved, you pass this data into your document loader to build your index.

It does. You can define the server tools as part of your agent's knowledge retrieval loop to allow for both reading and writing lead data.

Yes, once the agent decides on an action, it calls `update_lead` or `stop_playbook`. The changes are reflected in your Mav account immediately.

Data is transmitted over encrypted channels only when the agent requests it. No lead information is stored permanently in the index without your explicit configuration.

The server exposes contact details and interaction history. This sensitive lead information is handled only by the tools you authorize within your agent.

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