How to Use the Dexatel MCP in LlamaIndex

Q: How do I load Dexatel tools into my LlamaIndex FunctionAgent?

Install llama-index-tools-mcp. Set up a BasicMCPClient, wrap it in an McpToolSpec, and call totoollistasync() before passing the tools to your agent.

Index your Dexatel SMS history and build semantic search RAG pipelines with LlamaIndex.

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Connect Dexatel MCP to LlamaIndex

Create your Vinkius account to connect Dexatel 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 Dexatel with LlamaIndex

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Retrieve Semantic Messages in LlamaIndex

LlamaIndex changes how you interact with communication logs. Instead of writing SQL queries, your FunctionAgent calls `list_sms_messages` and indexes the raw JSON into your vector store. Once indexed, you run semantic searches across your entire text history. If a user asks about a specific campaign, the agent uses `search_sms_by_content` to pull exact matches, grounding its answers in real API data rather than guessing.

Ground RAG in Contact Profiles

Contact data is useless if your RAG pipeline cannot see it. Your agent executes `get_contact_profile` to pull full historical context on a user right before generating a response. Need to build an audience segment? The MCP Server integration lets you query `list_messaging_contacts`, feeding thousands of profiles directly into your document store for deep analysis.

Index Templates and Senders

Context matters for message generation. Before your agent drafts a reply, it pulls `list_sms_templates` to understand your brand's voice and approved formats. It also checks `list_authorized_sender_ids` to know which numbers are actually available. The agent uses this metadata to construct a valid `send_sms_message` request that the API will actually accept.

Setup guide

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

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Common questions about Dexatel MCP in LlamaIndex

Install `llama-index-tools-mcp`. Set up a `BasicMCPClient`, wrap it in an `McpToolSpec`, and call `to_tool_list_async()` before passing the tools to your agent.

Yes. The native search tool pulls exact keyword matches directly from the API, bypassing the need to index everything first.

You run the contact listing tool and pipe the output into your document ingestion pipeline. The agent handles the schema mapping automatically.

It does. You just include the sending tool in your allowed tools filter, and your agent can dispatch texts based on its retrieved context.

Never. The Vinkius architecture is entirely stateless. Message bodies touch the server for the duration of the API call and are immediately purged from memory.

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