How to Use the Telebroad MCP in LlamaIndex

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Connect Telebroad MCP to LlamaIndex

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Setup Telebroad with LlamaIndex

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Indexing Telebroad Communication Logs

LlamaIndex doesn't just run tools—it turns the results into searchable knowledge. You can query past communications by having your agent call `list_call_history`. That output gets indexed, making it part of a unified knowledge base. This means you stop hallucinating and start retrieving grounded answers based on actual Telebroad data.

Retrieving User Profiles for RAG

Need to know the context behind a contact? LlamaIndex indexes results from `get_profile`. You can then combine this structured user data with corporate documents in your vector store, allowing you to answer questions like, 'What was John's last call status?' This turns simple API calls into powerful, queryable knowledge assets.

Knowledge-Augmented Messaging

Instead of just sending an SMS blindly, your RAG application can first retrieve context. The agent pulls data using `list_sms_conversations` and then uses that summary to draft a highly specific message for the final `send_sms` tool call. This combination grounds the output, making every sent text accurate and relevant.

Setup guide

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Telebroad. 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.

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Common questions about Telebroad MCP in LlamaIndex

LlamaIndex takes raw API results—like call logs or fax lists—and embeds them. When you ask a question, it searches the semantic meaning of that data, not just keywords.

Yes. The MCP Server output is treated as verifiable data points. Whether it’s `list_faxes` or a user profile, the result becomes part of your searchable index.

The server can index structured communication records. This includes call history logs and details from `get_profile`, providing context on who spoke to whom and when.

Because the data is indexed, it provides accurate answers grounded in reality. You can ask about specific tools like `send_call` and get context from past executions of that tool.

It depends on the query, but both are excellent. Call logs (`list_call_history`) provide macro views, while `list_sms_conversations` gives granular text context.

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