How to Use the Bandwidth MCP in LlamaIndex

Q: Can LlamaIndex update Bandwidth data?

It can trigger tools like sendsms or deletemedia as part of your agent's workflow. These changes then reflect in the next index refresh.

Q: Does LlamaIndex support Bandwidth tool filtering?

You use the allowedtools filter to restrict which API operations your agent can access. This keeps your index focused on the data you need.

Index your Bandwidth messaging data into LlamaIndex to build RAG applications that answer questions grounded in your actual telecom logs.

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Connect Bandwidth MCP to LlamaIndex

Create your Vinkius account to connect Bandwidth 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 Bandwidth with LlamaIndex

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Index Bandwidth messages for semantic search

Feed the output of `list_messages` directly into your vector store using LlamaIndex. This lets your agent query past message logs to find specific communication trends or customer interactions. Instead of searching raw JSON, your agent performs semantic lookups. It finds relevant past messages based on intent, turning your log history into a searchable knowledge base.

Query Bandwidth campaigns with LlamaIndex

Load your 10DLC campaign details from `list_campaigns` into your index. Your agent can then answer questions about campaign status or configuration by retrieving facts directly from the API. Grounding your agent's answers in live data prevents hallucinations. It relies on the current state of your Bandwidth account rather than outdated training data.

Automate messaging data retrieval via MCP

Use the MCP tool spec to pull media lists or toll-free verification data into your agent's context. `list_media` and `get_toll_free` provide the raw material your RAG pipeline needs to function. Your application builds a unified index combining documents and API responses. The agent navigates this index to provide accurate, data-backed insights on your messaging infrastructure.

Setup guide

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

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Common questions about Bandwidth MCP in LlamaIndex

It converts API results into vector embeddings. You then query these embeddings to find specific messages or campaign details within your Bandwidth account.

It can trigger tools like `send_sms` or `delete_media` as part of your agent's workflow. These changes then reflect in the next index refresh.

Data is processed in memory during the indexing phase. It never leaves your local environment or the Vinkius secure sandbox.

You use the allowed_tools filter to restrict which API operations your agent can access. This keeps your index focused on the data you need.

All communication occurs over HTTPS with token-based auth. Your messaging logs remain protected throughout the RAG retrieval process.

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