How to Use the zipperHQ MCP in LlamaIndex

Q: How do I retrieve account details from zipperHQ using LlamaIndex?

Simply run getaccount and let the result get indexed. Your RAG application can then answer questions like, 'What is our billing contact email?' based on this stored data. This turns a single API call into permanent knowledge.

Q: Can I search video details with zipperHQ using LlamaIndex?

Yes. You run getvideo for specific videos, and those details get added to the searchable index. Later, you can query by date, title, or associated contact. This makes finding old information much faster than searching through raw logs.

Q: How do I check the status of zipperHQ with LlamaIndex?

Call checkzipperstatus to get the current operational state. Indexing this confirms the service's health at that point in time. This is useful for creating uptime monitoring knowledge bases.

Q: Does zipperHQ track viewing metrics with LlamaIndex?

Yes, through getcontactviews and getvideoanalytics. These view counts are indexed as quantitative data points. This allows you to ask sophisticated questions like, 'Show me contacts who viewed videos more than 10 times last month.'

Build knowledge bases from zipperHQ communications using LlamaIndex.

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Connect zipperHQ MCP to LlamaIndex

Create your Vinkius account to connect zipperHQ 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 zipperHQ with LlamaIndex

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Index Contact and Video Data

Use `list_contacts` to pull a list of all video recipients. Indexing this list means your RAG application can answer questions like, 'Which contacts haven't been reached in 6 months?' The MCP Server output becomes searchable knowledge; you query past API results instead of hallucinating.

Semantic Search for Videos

When you run `search_videos`, the results are indexed into a vector store. This lets your agent answer, 'What kind of videos did we send about Q3 budgeting?' based on keywords. The knowledge base combines live API data with documents, giving grounded answers.

Track Video Performance History

Querying `get_video_analytics` and indexing the results means you can ask about performance trends over time. You're not just getting a single report; you’re building historical context. The LlamaIndex application combines this metric data with other records, making every API call part of a unified knowledge index.

Setup guide

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

Simply run `get_account` and let the result get indexed. Your RAG application can then answer questions like, 'What is our billing contact email?' based on this stored data. This turns a single API call into permanent knowledge.

Yes. You run `get_video` for specific videos, and those details get added to the searchable index. Later, you can query by date, title, or associated contact. This makes finding old information much faster than searching through raw logs.

The server provides videos, recordings, and contacts. When indexed, these become queryable entities—you can ask to compare video analytics against contact lists. The resulting index is a combination of structured API data and unstructured text.

Call `check_zipper_status` to get the current operational state. Indexing this confirms the service's health at that point in time. This is useful for creating uptime monitoring knowledge bases.

Yes, through `get_contact_views` and `get_video_analytics`. These view counts are indexed as quantitative data points. This allows you to ask sophisticated questions like, 'Show me contacts who viewed videos more than 10 times last month.'

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