How to Use the LlamaIndex (AI Data Framework & RAG) MCP in LlamaIndex

Q: Can I use LlamaIndex (AI Data Framework & RAG) to query remote pipelines from a local LlamaIndex agent?

Yes, you use the MCP tool spec to expose querypipeline to your local agent. The agent can then search your remote LlamaCloud indexes and use that context to answer questions, combining local and cloud-hosted data.

Q: How does a local LlamaIndex agent discover available projects in LlamaIndex (AI Data Framework & RAG)?

The agent calls listprojects to get a list of active projects, then uses listpipelines to see the pipelines inside those projects. This lets the agent dynamically decide which pipeline is best suited for a specific user query.

Q: Can I index the output of LlamaIndex (AI Data Framework & RAG) MCP tools back into a local vector store?

Yes, you can capture the text returned by querypipeline or the file list from listfiles and feed it directly into a local document store. This allows your agent to build a searchable history of its interactions with your remote pipelines.

Q: What package is required to connect to this server?

You need to install llama-index-tools-mcp via pip. Once installed, initialize the basic MCP client with your Vinkius endpoint and convert the tools using the tool spec helper.

Build self-indexing LlamaIndex agents that query remote RAG pipelines and index the results for semantic search.

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Connect LlamaIndex (AI Data Framework & RAG) MCP to LlamaIndex

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Setup LlamaIndex (AI Data Framework & RAG) with LlamaIndex

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Index active pipeline outputs into your local agent

This integration lets your local LlamaIndex agent query remote RAG pipelines and feed those results straight into a local vector index. By using `query_pipeline`, the agent pulls fresh, grounded data from LlamaCloud and immediately indexes the output, making past queries searchable in future agent loops. You can also use `list_files` to monitor which source files are currently ingested. This allows your LlamaIndex agent to verify what documents are live before deciding to trigger a new search or update its internal knowledge base.

Manage remote LlamaCloud projects through your agent

Give your LlamaIndex agent administrative control over your remote data pipelines. Using `list_projects` and `list_pipelines`, your agent can discover active projects, check their configurations with `get_pipeline`, and select the most relevant index to query without manual intervention. This turns your LlamaIndex agent into an active manager of your data. Instead of hardcoding pipeline IDs, the agent inspects the available resources dynamically using this MCP Server and adapts its search strategy based on what it finds.

Secure your LlamaIndex agent with tool filtering

You do not have to expose every tool to your agent. Use the `allowed_tools` filter in the MCP tool spec to restrict access to read-only operations like `query_pipeline` or `list_indexes`, keeping management tools like pipeline configuration hidden. This ensures your LlamaIndex agent stays focused on retrieving information. It prevents accidental configuration changes while still giving the model access to the exact data sources it needs to answer user queries.

Setup guide

Set up LlamaIndex (AI Data Framework & RAG) 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 LlamaIndex (AI Data Framework & RAG) 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 LlamaIndex (AI Data Framework & RAG) tools.",
)
response = await agent.run("List recent LlamaIndex (AI Data Framework & RAG) data")

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Common questions about LlamaIndex (AI Data Framework & RAG) MCP in LlamaIndex

Yes, you use the MCP tool spec to expose `query_pipeline` to your local agent. The agent can then search your remote LlamaCloud indexes and use that context to answer questions, combining local and cloud-hosted data.

The agent calls `list_projects` to get a list of active projects, then uses `list_pipelines` to see the pipelines inside those projects. This lets the agent dynamically decide which pipeline is best suited for a specific user query.

Yes, you can capture the text returned by `query_pipeline` or the file list from `list_files` and feed it directly into a local document store. This allows your agent to build a searchable history of its interactions with your remote pipelines.

You need to install `llama-index-tools-mcp` via pip. Once installed, initialize the basic MCP client with your Vinkius endpoint and convert the tools using the tool spec helper.

Your pipeline metadata, raw file lists, and query inputs are processed inside ephemeral, zero-trust sandboxes. Vinkius secures these connections using single-token authentication, ensuring that your raw document content and pipeline structures are never cached or exposed to third parties.

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