How to Use the MessageBird MCP in LlamaIndex

Q: How do I load MessageBird MCP tools into LlamaIndex?

First, run pip install llama-index-tools-mcp to get the integration package. Initialize BasicMCPClient with your Vinkius URL, wrap it in McpToolSpec, and call totoollistasync() to pass the tools to your FunctionAgent.

Q: Can I use LlamaIndex to semantically search my MessageBird SMS history?

Yes, you can. Your pipeline can call listmessages to retrieve recent SMS dispatches, convert those text payloads into document nodes, and index them into a vector store for semantic querying.

Q: How does LlamaIndex handle MessageBird contact groups for targeted RAG?

The agent uses listgroups to find specific customer segments. It then retrieves the associated contacts via getgroup to restrict its vector search or messaging focus to that specific audience.

Q: How do I verify if a phone number is valid before indexing it?

You can have your agent trigger listhlr to run a network lookup on the number. This ensures you only index and target active, deliverable phone numbers in your database.

Index MessageBird contact lists and SMS logs directly into LlamaIndex vector stores for semantic search.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect MessageBird MCP to LlamaIndex

Create your Vinkius account to connect MessageBird 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.

GDPR Free for Subscribers

Setup MessageBird with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Turn MessageBird API logs into a LlamaIndex RAG index

Ingesting `list_messages` and `get_message` outputs directly into your LlamaIndex vector store stops you from searching raw logs. This MCP Server allows your pipeline to ingest message data directly, feeding historical SMS texts straight into your index. Once indexed, you can query your past communication history using natural language. The system retrieves actual message records instead of guessing or hallucinating what you sent to a customer.

Search and ground your queries in live contact data

Using `list_contacts` and `get_contact` lets your LlamaIndex agent pull fresh data directly into your RAG pipeline retrieval step. This keeps your customer context completely accurate during live interactions. This prevents your system from relying on stale, cached database records. Every query about customer status is grounded in real-time information retrieved straight from the API.

Build knowledge-augmented messaging workflows

Querying `get_group` and `list_groups` helps your LlamaIndex agent determine the most relevant document template to send. Your agent can query your documents and then instantly decide how to route messages based on segment membership via MCP. After selecting the right message, the agent executes `send_sms` to dispatch it. This connects your static company knowledge base directly to active, outbound communication channels.

Setup guide

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by MessageBird. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect MessageBird now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about MessageBird MCP in LlamaIndex

First, run `pip install llama-index-tools-mcp` to get the integration package. Initialize `BasicMCPClient` with your Vinkius URL, wrap it in `McpToolSpec`, and call `to_tool_list_async()` to pass the tools to your `FunctionAgent`.

Yes, you can. Your pipeline can call `list_messages` to retrieve recent SMS dispatches, convert those text payloads into document nodes, and index them into a vector store for semantic querying.

The agent uses `list_groups` to find specific customer segments. It then retrieves the associated contacts via `get_group` to restrict its vector search or messaging focus to that specific audience.

You can have your agent trigger `list_hlr` to run a network lookup on the number. This ensures you only index and target active, deliverable phone numbers in your database.

Yes, because Vinkius handles all API requests within highly secure, ephemeral V8 isolates. Your HLR network lookups and contact phone numbers are never cached or written to disk, ensuring strict compliance with communication privacy standards.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript