How to Use the Ziflow MCP in LlamaIndex

Q: How do I use Ziflow with LlamaIndex to recall past proof details?

You first run searchproofs and then index the results. When you query your application, it searches the indexed data for specific proofs that match complex criteria.

Q: Can I use Ziflow and LlamaIndex for compliance auditing?

Yes. By indexing the results of getaccountinfo, you build a permanent record of your system's setup. You can then query this index to confirm specific settings or user roles.

Q: How do I verify contact information with Ziflow and LlamaIndex?

Use getcontactbyemail to get the current data, and immediately index that output. This ensures your knowledge base always has an accurate record of key contacts.

Q: If I need to know about webhooks with Ziflow and LlamaIndex, what data type is stored?

This server touches webhook configuration data. You use listwebhooks to pull the list, and then indexing this output allows your application to answer questions about which webhooks are active or what they are designed for.

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Connect Ziflow MCP to LlamaIndex

Create your Vinkius account to connect Ziflow 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 Ziflow with LlamaIndex

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Search and index proof metadata.

The first step is running `list_integration_properties` to gather all the available metadata about your proofs. You then index this output into a vector store. Later, you query that knowledge base using LlamaIndex. Instead of just listing properties, you ask complex questions like 'Which proof has high-res images and was edited last week?'

Index system settings for deep recall.

Run `get_account_info` to capture the current account profile data. This output is indexed, making all your billing or structural details searchable. Your RAG application can now answer questions like 'What was our primary contact email?' without needing a live API call—it reads from its own knowledge index.

Search and query proof history.

First, use `search_proofs` to pull records of past reviews. The critical step is indexing this result set into your vector store. This means you can't just search by date; you can ask the knowledge base to find 'all proofs related to Q3 marketing campaigns that required legal sign-off,' getting answers grounded in real API data.

Setup guide

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

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Common questions about Ziflow MCP in LlamaIndex

You first run `search_proofs` and then index the results. When you query your application, it searches the indexed data for specific proofs that match complex criteria.

LlamaIndex allows you to treat proof metadata and account information as a searchable knowledge base. You get answers grounded in the actual API data returned by tools like `list_integration_properties`.

Yes. By indexing the results of `get_account_info`, you build a permanent record of your system's setup. You can then query this index to confirm specific settings or user roles.

Use `get_contact_by_email` to get the current data, and immediately index that output. This ensures your knowledge base always has an accurate record of key contacts.

This server touches webhook configuration data. You use `list_webhooks` to pull the list, and then indexing this output allows your application to answer questions about which webhooks are active or what they are designed for.

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