How to Use the MagicBell MCP in LlamaIndex

Build searchable notification knowledge bases in LlamaIndex using this MCP Server to index your alert history.

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Connect MagicBell MCP to LlamaIndex

Create your Vinkius account to connect MagicBell 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 MagicBell with LlamaIndex

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Index broadcast history in LlamaIndex

The `list_broadcasts` tool retrieves your full alert history. By piping this into your LlamaIndex vector store, you make every past notification queryable via natural language. This allows your RAG application to answer questions about past system states. You can ask your agent when a specific alert was sent or what it contained.

Verify alert status with get_broadcast

Use `get_broadcast` to ground your agent in real-time API data. Instead of guessing, your LlamaIndex agent fetches the current status of any notification directly from the source. This creates a feedback loop where your knowledge base stays updated. Your agent provides answers based on the actual delivery status of your broadcasts.

Automate alerts with create_broadcast

The `create_broadcast` tool lets your agent send notifications as part of its data processing task. You can trigger alerts based on the results of a semantic search. This bridges the gap between your indexed data and your communication layer. Your agent acts on findings by notifying the right people instantly.

Setup guide

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

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Common questions about MagicBell MCP in LlamaIndex

Use the MCP client to fetch broadcast lists and index them into your vector store. Your agent then performs semantic searches over this data to answer user queries.

Absolutely. Simply initialize the MCP client, convert the tools to a tool list, and pass them to your agent. This allows your agent to interact with your notification infrastructure.

It provides live operational data to your index. Instead of relying on static documents, your agent queries real-time notification logs to provide accurate, up-to-date answers.

They are. Once you list your broadcasts and index the output, your agent can search through historical notification payloads to find specific events or patterns.

The MCP Server uses secure authentication tokens for every request. Your notification payloads are never cached in plaintext within your index, keeping sensitive alert content protected.

Use it with your favorite AI tools

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

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