How to Use the DoubleTick MCP in LlamaIndex

Q: How do I index DoubleTick message histories in LlamaIndex using the MCP Server?

You load conversation logs using listconversationmessages and parse them into vector store documents. This makes your customer chat history fully searchable by your RAG agents.

Q: Can I search DoubleTick contact lists semantically with LlamaIndex?

Yes. Your pipeline indexes the contacts pulled from listwhatsappcontacts. You can then perform vector queries to group contacts based on profile attributes.

Q: How does LlamaIndex select the right DoubleTick template?

Your agent queries listapprovedtemplates using semantic search. It finds the template that matches the user's intent and sends it via sendwhatsapptemplate.

Q: Can I monitor delivery rates of my RAG-generated messages?

You can use quickwhatsappvolumeaudit to get a high-level summary of messaging activity. This helps you track success rates directly inside your index runs.

Index WhatsApp histories and query customer contact records directly inside your LlamaIndex RAG pipelines.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect DoubleTick MCP to LlamaIndex

Create your Vinkius account to connect DoubleTick 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 DoubleTick with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Build WhatsApp Knowledge Bases with LlamaIndex

`list_conversation_messages` pulls raw conversation histories directly into your vector index. This MCP Server lets your agent query past user interactions to ground its responses. Querying past chats ensures your RAG pipeline doesn't hallucinate customer preferences. Live API data is combined with documents to build a unified index. Your agent gets answers grounded in actual conversation history, not guesses. It makes your customer interactions significantly more accurate.

Index Contact Records and Opt-In Statuses

`list_whatsapp_contacts` retrieves your entire directory to feed your vector store. Your agent indexes these profiles alongside `list_opted_in_contacts` to maintain accurate records. Segmenting active users becomes a simple search query. Semantic search over user profiles is enabled using `search_contacts_by_keyword`. Finding a specific customer segment takes just one step. Your RAG applications target the right users based on actual profile data.

Query Active WhatsApp Templates and Account Limits

`list_approved_templates` indexes your pre-approved WhatsApp templates for semantic retrieval. This MCP integration allows your agent to match user queries with the correct template. Once matched, the message is sent instantly via `send_whatsapp_template`. Operational limits are monitored via `get_doubletick_account_metadata`. Your agent avoids executing actions when limits are reached. This safeguard protects your API quota from accidental depletion.

Setup guide

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

You load conversation logs using `list_conversation_messages` and parse them into vector store documents. This makes your customer chat history fully searchable by your RAG agents.

Yes. Your pipeline indexes the contacts pulled from `list_whatsapp_contacts`. You can then perform vector queries to group contacts based on profile attributes.

Your agent queries `list_approved_templates` using semantic search. It finds the template that matches the user's intent and sends it via `send_whatsapp_template`.

You can use `quick_whatsapp_volume_audit` to get a high-level summary of messaging activity. This helps you track success rates directly inside your index runs.

The server processes your WhatsApp templates and contact lists entirely in memory. Data is transmitted securely and never written to persistent storage, maintaining isolation.

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