How to Use the Videco MCP in LlamaIndex

Q: How can I use LlamaIndex to search through Videco campaigns?

You run listcampaigns via the MCP Server, and then you index that output into your vector store. This lets you query campaign details semantically later on, bypassing the need for exact matching.

Q: If I call Videco through LlamaIndex, what data is indexed?

The output from tools like getvideo or createcampaign becomes the knowledge. You're not just running a function; you're capturing and storing the factual results of that function call for later retrieval.

Q: What specific information does this MCP Server touch for privacy?

This server touches on lead details. The getlead tool provides access to detailed customer profiles, which are then indexed into your knowledge graph.

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Connect Videco MCP to LlamaIndex

Create your Vinkius account to connect Videco 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 Videco with LlamaIndex

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Search Video Performance Data via MCP Server

You can index raw API data into a searchable knowledge base. Run `get_video_analytics` to pull performance metrics, then store those results in your LlamaIndex vector store. Later, you query the index and get answers grounded in actual video data, not just general descriptions.

Track Campaign History with Videco

Need details on a past campaign? Use `get_campaign` to fetch specific campaign records. Indexing this output means you can query your knowledge base later and ask things like, 'What was the budget for Campaign X last quarter?'—and get an answer based on real data.

Manage Lead Information in Your Knowledge Base

Instead of just calling `list_leads`, you can pass that list to LlamaIndex and index it. This treats lead data as searchable knowledge. You'll build RAG applications where a user asks, 'Find all leads from the Northeast region,' and the system answers using your indexed Videco data.

Setup guide

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

You run `list_campaigns` via the MCP Server, and then you index that output into your vector store. This lets you query campaign details semantically later on, bypassing the need for exact matching.

This server deals with lead details and video analytics. When using LlamaIndex, these outputs become part of a unified index, allowing you to search both structured and semi-structured API information.

The output from tools like `get_video` or `create_campaign` becomes the knowledge. You're not just running a function; you're capturing and storing the factual results of that function call for later retrieval.

Yes. By grounding your queries in live API data through this MCP Server, you eliminate hallucinations because every answer is sourced directly from the actual campaign or lead records.

This server touches on lead details. The `get_lead` tool provides access to detailed customer profiles, which are then indexed into your knowledge graph.

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