How to Use the ArcXP MCP in LlamaIndex

Q: How do I index ArcXP articles in LlamaIndex?

Call getanscontent to retrieve the payload and pass it to your LlamaIndex document loader. The agent then adds this content to your vector store.

Q: Can I search ArcXP photo libraries with LlamaIndex?

Use the searchphotoassets tool to find media. Once retrieved, you can index the metadata to help your agent find photos based on semantic descriptions.

Index your newsroom data into searchable knowledge bases using ArcXP and LlamaIndex.

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Connect ArcXP MCP to LlamaIndex

Create your Vinkius account to connect ArcXP 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 ArcXP with LlamaIndex

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Index ArcXP content for RAG in LlamaIndex

Feed the output of `get_ans_content` directly into your LlamaIndex vector store. This turns your static CMS assets into a queryable knowledge base for your agents. Your agent now answers questions grounded in your actual newsroom history. You stop guessing and start retrieving exact article data.

Query newsroom assets with LlamaIndex

Use `search_photo_assets` and `search_ans_content` to pull data for your RAG pipeline. LlamaIndex then indexes these results to maintain a live connection to your content. This approach removes the need for manual data imports. Your index stays current because your agent fetches fresh data whenever it runs a search.

Automate asset retrieval in LlamaIndex

Configure your agent to pull specific video or photo metadata using `get_video_asset`. The server formats the response so your agent can index it without extra parsing. Everything happens in memory as the agent builds its understanding of your newsroom. You get a unified view of your assets through your existing index.

Setup guide

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

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Common questions about ArcXP MCP in LlamaIndex

Call `get_ans_content` to retrieve the payload and pass it to your LlamaIndex document loader. The agent then adds this content to your vector store.

Use the `search_photo_assets` tool to find media. Once retrieved, you can index the metadata to help your agent find photos based on semantic descriptions.

Your content is stored only within your private vector index. The MCP server acts as a secure, ephemeral bridge between the CMS and your local environment.

The server returns clear error messages if a tool call fails. You can catch these in your agent logic to prevent corrupted data from entering your index.

The server processes only the raw JSON payload of your article metadata. Your sensitive CMS credentials remain hidden behind the Vinkius endpoint token.

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