How to Use the UnifyApps MCP in LlamaIndex

Q: How do I search past flow history using UnifyApps and LlamaIndex?

You can use listflowexecutions to get the raw data, which LlamaIndex then indexes. This lets you query historical records and get answers grounded in actual execution logs.

Q: What details about my integrations can UnifyApps and LlamaIndex provide?

The listintegrations tool provides the list, which LlamaIndex then turns into searchable knowledge. You query these results to understand specific setup parameters.

Q: Can I search for configured AI agents with UnifyApps and LlamaIndex?

Yes. The listaiagents tool feeds agent data into the index, allowing you to perform semantic searches across your documentation and system logs.

Q: Does UnifyApps and LlamaIndex help audit connections?

You use listactiveconnections. LlamaIndex indexes this connection data, letting you ask questions about specific accounts that were active at a certain time.

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Setup UnifyApps with LlamaIndex

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Query Automation Flow History

You can use `list_flow_executions` to get a history of every run. LlamaIndex indexes this data, meaning you can search past outcomes and configurations for answers. The agent doesn't just list results; it takes the execution details and grounds them in your vector store. This means you query 'Why did flow X fail last week?' and get an answer based on actual API logs.

Index Platform Integrations

The `list_integrations` tool provides a list of all configured integrations. By indexing this data, LlamaIndex lets you search for integration types or names across past documents. Instead of just seeing a list, the system treats those integration details as knowledge. You can ask about specific setup parameters and get an answer derived from your live API data.

Audit Agents and Connections

Want to know what's running? Use `list_ai_agents` or `list_active_connections`. LlamaIndex indexes these lists, so you can query historical documents for mentions of specific agents or connection accounts. This makes auditing simple. You build a unified index combining system configurations (like agent names) with your operational notes.

Setup guide

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

You can use `list_flow_executions` to get the raw data, which LlamaIndex then indexes. This lets you query historical records and get answers grounded in actual execution logs.

The `list_integrations` tool provides the list, which LlamaIndex then turns into searchable knowledge. You query these results to understand specific setup parameters.

Yes. The `list_ai_agents` tool feeds agent data into the index, allowing you to perform semantic searches across your documentation and system logs.

You use `list_active_connections`. LlamaIndex indexes this connection data, letting you ask questions about specific accounts that were active at a certain time.

This server touches connection metadata. You can check the status of linked accounts by calling `list_active_connections` and indexing those results for later querying.

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