How to Use the Zixflow MCP in LlamaIndex

Q: Can I get historical context using Zixflow and LlamaIndex?

Yes. By indexing the results of tools like listcollectionrecords, you can query past sessions or configurations, grounding answers in real API data instead of just memory.

Q: Does LlamaIndex support filtering Zixflow resources?

It does. The client setup allows for an allowedtools filter and includeresources=True, letting you precisely manage what kind of sales collections or records are indexed.

Turn Zixflow data into knowledge indexes for LlamaIndex’s RAG capabilities.

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Connect Zixflow MCP to LlamaIndex

Create your Vinkius account to connect Zixflow 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 Zixflow with LlamaIndex

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Build a searchable knowledge base with the MCP Server.

LlamaIndex doesn't just call tools; it takes the output and indexes it so you can query it later. If your agent runs `get_record_details` for an account, that detailed record becomes part of a searchable knowledge store. This means you can ask questions like, 'What was the status of the Acme Corp record last month?' and get an answer grounded in actual Zixflow API data.

Index sales collections using LlamaIndex.

You can combine live API data with documents into a unified index. For instance, you run `list_collection_records` to gather recent contacts and then feed that structured list into the vector store. The resulting knowledge base lets you query across both your internal docs *and* current sales records—no hallucinations necessary.

Query transaction history with LlamaIndex MCP Server.

Need to know about past activity? You can use the server's tools, like `list_wallet_transactions`, and index that entire output. Later, you query your index for 'all transactions over $5k last quarter,' getting a precise answer. It’s turning temporary API calls into permanent, actionable knowledge.

Setup guide

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

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Common questions about Zixflow MCP in LlamaIndex

When you query the MCP Server, LlamaIndex captures the structured output—whether it's a company record or a list of transactions—and indexes that text into your vector store for semantic search.

Yes. By indexing the results of tools like `list_collection_records`, you can query past sessions or configurations, grounding answers in real API data instead of just memory.

It does. The client setup allows for an allowed_tools filter and include_resources=True, letting you precisely manage what kind of sales collections or records are indexed.

You're indexing structured data like People/Company collection records and wallet transactions. These API outputs become the searchable knowledge base for your RAG application.

The server deals with sales collections, contact details, and transaction records. You are indexing this structured API output into a knowledge base, which provides context but requires careful handling.

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