How to Use the Dixa MCP in LlamaIndex

Q: How do I load Dixa data with this MCP Server?

Use llama-index-tools-mcp to initialize the client. Call totoollistasync() to fetch the tools, then use them to load conversation data from listconversations into your index.

Q: Can LlamaIndex update customer records in Dixa?

Yes. Your pipeline can use createcustomerprofile to add new users to your support database during indexing runs.

Q: How does LlamaIndex prevent stale data when querying Dixa?

You can set up a query pipeline that calls getconversationdetails before answering. This ensures your agent uses the latest ticket updates instead of outdated vector embeddings.

Q: Can I filter which tools LlamaIndex can access?

Yes, you can use the allowedtools filter. This lets you restrict the agent to read-only tools like listconversations while blocking write tools.

Index Dixa support history into LlamaIndex vector stores to search and query live ticket data without hallucinations.

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Connect Dixa MCP to LlamaIndex

Create your Vinkius account to connect Dixa 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 Dixa with LlamaIndex

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Index active Dixa conversations for RAG

This MCP Server provides `list_conversations` to feed live customer tickets directly into your LlamaIndex document store. Your agent indexes these conversations to make the entire support queue searchable via semantic queries. Instead of guessing past issues, your RAG pipeline queries `get_conversation_details` to pull exact historical contexts. This grounds your agent's answers in actual resolved tickets rather than training data.

Search customer profiles using LlamaIndex

The MCP Server exposes `list_end_users` to let you build a queryable vector index of your customer base. LlamaIndex matches incoming user questions with existing profile records instantly. If a user isn't found, the pipeline triggers `create_customer_profile` to insert them into your support database. This keeps your indexed customer knowledge base updated in real time.

Track team availability and health status

Use `get_connection_status` to verify that your API connection is healthy before executing heavy index updates. This prevents your pipeline from attempting to index empty or failed payloads. You can also query `list_support_teams` to index your current team structures. This allows your query engine to answer questions about which team handles specific customer segments.

Setup guide

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

Use `llama-index-tools-mcp` to initialize the client. Call `to_tool_list_async()` to fetch the tools, then use them to load conversation data from `list_conversations` into your index.

Yes. Your pipeline can use `create_customer_profile` to add new users to your support database during indexing runs.

You can set up a query pipeline that calls `get_conversation_details` before answering. This ensures your agent uses the latest ticket updates instead of outdated vector embeddings.

Yes, you can use the `allowed_tools` filter. This lets you restrict the agent to read-only tools like `list_conversations` while blocking write tools.

All customer profiles and end-user data retrieved via the MCP Server are processed in memory within isolated V8 engines. Vinkius never caches your customer records or API tokens on disk.

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