How to Use the Clickatell MCP in LlamaIndex

Q: Can I use LlamaIndex to query Clickatell templates?

Yes. You pull the list using listwhatsapptemplates and index them. This helps your agent choose the right format for WhatsApp sends.

Index your Clickatell message logs directly into LlamaIndex for semantic retrieval and context-aware messaging.

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Connect Clickatell MCP to LlamaIndex

Create your Vinkius account to connect Clickatell 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 Clickatell with LlamaIndex

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Index Clickatell logs for RAG

The tool outputs from `list_message_history` go straight into your LlamaIndex vector store. You query your past communication history using natural language. This creates a searchable database of your messaging activity. LlamaIndex keeps these records current as you add more data.

Query account details in LlamaIndex

Ask questions about your profile using `get_account_info`. LlamaIndex grounds the answer in the live data pulled from the API. This removes the need for manual lookups. You get accurate, real-time facts about your setup.

Update knowledge with message status

Use `get_message_status` to refresh your index with the latest delivery metrics. LlamaIndex then reflects these updates in your RAG app. It keeps your agent informed. You stop relying on stale data for your decision-making.

Setup guide

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

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Common questions about Clickatell MCP in LlamaIndex

It converts the history list into vector embeddings. You then perform semantic searches to find specific past interactions.

Yes. You pull the list using `list_whatsapp_templates` and index them. This helps your agent choose the right format for WhatsApp sends.

Only the data you choose to index is stored. The MCP connection ensures that raw credentials are never persisted in your vector store.

The agent receives an error message instead of the indexable output. You can then configure a retry or a fallback path.

The server isolates your message metadata within the Vinkius environment. It only exposes the specific fields required for your search index.

Use it with your favorite AI tools

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

OpenAI Agents SDK sdk-python

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Pydantic AI sdk-python

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