How to Use the Kavkom MCP in LlamaIndex

Q: How does LlamaIndex handle Kavkom SMS histories?

The listsmshistory tool pulls the raw text payloads. LlamaIndex chunks these messages and embeds them, letting you search past conversations by topic rather than just timestamp.

Index your Kavkom call histories and SMS logs into searchable RAG applications with LlamaIndex and this MCP Server.

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Connect Kavkom MCP to LlamaIndex

Create your Vinkius account to connect Kavkom 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 Kavkom with LlamaIndex

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Index Your Cloud Telephony Data

Phone records usually sit in isolated dashboards. With LlamaIndex and this MCP integration, you pull everything using `list_calls` and `list_sms_history`. The framework ingests these raw logs and converts them into vector embeddings. Your team can now run semantic searches across thousands of interactions. Instead of manually filtering by date, you ask the agent to find all missed calls from specific European regions. The answers are grounded in actual API data.

Ground Context with CRM Sync

RAG applications need accurate identity mapping. By running `list_crm_contacts`, your application pulls the latest customer profiles into the index. It matches phone numbers to real company names. When a user queries a specific account, the agent retrieves the exact contact details. If a rep needs to add a new lead, they invoke `create_contact` directly through the chat interface.

Query Team Activity via the Kavkom MCP Server

Management needs answers about support volume. The agent runs `list_team_members` to map out your staff structure. It then correlates that with `get_call_details` to build an index of who handled what. You ask questions like 'Who handled the most SMS responses yesterday?' The agent retrieves the exact documents from the vector store and synthesizes a factual answer. No hallucinations, just hard metrics.

Setup guide

Set up Kavkom 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 Kavkom 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 Kavkom tools.",
)
response = await agent.run("List recent Kavkom 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 Kavkom. 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 Kavkom MCP in LlamaIndex

Install llama-index-tools-mcp and initialize BasicMCPClient. Wrap it with McpToolSpec to expose the telephony endpoints to your FunctionAgent.

It queries whatever you index. You can set up a routine that runs get_call_details hourly. The agent then searches that updated vector store to answer your questions.

The list_sms_history tool pulls the raw text payloads. LlamaIndex chunks these messages and embeds them, letting you search past conversations by topic rather than just timestamp.

Yes. While LlamaIndex excels at search, its agents can still execute write actions. The agent can trigger send_sms_message if it determines a customer needs an immediate reply.

The server transfers phone numbers, names, and SMS contents to your client. LlamaIndex stores this data in your local or hosted vector database. You control access to that index entirely on your end.

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