Zixflow MCP Server for LlamaIndex 7 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Zixflow as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Zixflow. "
"You have 7 tools available."
),
)
response = await agent.run(
"What tools are available in Zixflow?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Zixflow MCP Server
Connect your Zixflow workspace to any AI agent to automate your sales and CRM operations. This MCP server enables your agent to interact with collections (People, Company, etc.), manage individual records, and track wallet transactions directly from natural language.
LlamaIndex agents combine Zixflow tool responses with indexed documents for comprehensive, grounded answers. Connect 7 tools through the Vinkius and query live data alongside vector stores and SQL databases in a single turn — ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Collection Oversight — List all data collections configured in your workspace to understand your CRM structure
- Contact Management — List, retrieve, create, and update records within any collection using detailed field mappings
- Precision Filtering — Search for specific records using JSON-based filtering and sorting criteria
- Cleanup Automation — Delete unnecessary records and maintain your database directly via natural language commands
- Wallet Tracking — Access a history of transactions and balance changes within your Zixflow wallet
The Zixflow MCP Server exposes 7 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Zixflow to LlamaIndex via MCP
Follow these steps to integrate the Zixflow MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 7 tools from Zixflow
Why Use LlamaIndex with the Zixflow MCP Server
LlamaIndex provides unique advantages when paired with Zixflow through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Zixflow tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Zixflow tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Zixflow, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Zixflow tools were called, what data was returned, and how it influenced the final answer
Zixflow + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Zixflow MCP Server delivers measurable value.
Hybrid search: combine Zixflow real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Zixflow to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Zixflow for fresh data
Analytical workflows: chain Zixflow queries with LlamaIndex's data connectors to build multi-source analytical reports
Zixflow MCP Tools for LlamaIndex (7)
These 7 tools become available when you connect Zixflow to LlamaIndex via MCP:
create_collection_record
g., a person or company) to a specific Zixflow collection. Create a new record in a collection
delete_collection_record
Delete a record from a collection
get_record_details
Get details for a specific record
list_collection_records
Requires a JSON body for filtering/sorting. List records within a specific collection
list_collections
List all collections (People, Company, etc.)
list_wallet_transactions
List Zixflow wallet transactions
update_collection_record
Update an existing record
Example Prompts for Zixflow in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Zixflow immediately.
"List all data collections in my Zixflow workspace."
"Show details for record with ID '98765'."
"List my recent wallet transactions."
Troubleshooting Zixflow MCP Server with LlamaIndex
Common issues when connecting Zixflow to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpZixflow + LlamaIndex FAQ
Common questions about integrating Zixflow MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Zixflow with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Zixflow to LlamaIndex
Get your token, paste the configuration, and start using 7 tools in under 2 minutes. No API key management needed.