How to Use the Atlas MCP in LlamaIndex

Q: Does LlamaIndex support Atlas resource discovery?

Enable includeresources in your setup. This lets the agent see the available help center articles as raw text for your RAG model.

Index your Atlas support history into LlamaIndex for grounded, accurate AI answers.

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Connect Atlas MCP to LlamaIndex

Create your Vinkius account to connect Atlas 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 Atlas with LlamaIndex

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Knowledge-augmented Atlas data for LlamaIndex

Turn your support history into a searchable index. LlamaIndex runs `list_tickets` and stores the output in a vector store for semantic retrieval. Your RAG pipeline now pulls from actual customer interactions. No more guessing what the user meant.

Querying Atlas via LlamaIndex agents

The agent uses `get_customer` to fetch specific profiles during a conversation. This data gets embedded alongside your existing docs. You get responses grounded in real API data. The agent knows exactly which ticket belongs to which client.

Filtering Atlas tools in LlamaIndex

Use the allowed_tools filter to limit what the agent can touch. You might restrict it to read-only access for `list_articles` while keeping write tools disabled. This keeps your index clean. You define exactly what data enters your vector store from the Atlas platform.

Setup guide

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

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Common questions about Atlas MCP in LlamaIndex

Use the McpToolSpec to convert the server tools into a format the agent understands. Then run your standard ingestion pipeline to vectorise the results.

Enable include_resources in your setup. This lets the agent see the available help center articles as raw text for your RAG model.

The data transit is encrypted and ephemeral. LlamaIndex only processes the specific snippets you explicitly choose to index.

Vinkius manages the endpoint token for you. You just provide the URL to the BasicMCPClient and the connection is established.

We enforce strict zero-trust principles. Your support logs are never stored by Vinkius, ensuring only your local index holds the history.

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