How to Use the Aero Workflow MCP in LlamaIndex

Q: Is it possible to limit Aero Workflow tools in LlamaIndex?

Yes, use the allowedtools filter during setup. This ensures your agent only uses the tools necessary for your specific RAG application.

Index your Aero Workflow data into LlamaIndex to query accounting records using natural language.

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Connect Aero Workflow MCP to LlamaIndex

Create your Vinkius account to connect Aero Workflow 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 Aero Workflow with LlamaIndex

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Build a knowledge base from Aero Workflow

Convert results from `list_workflow_tasks` into a searchable vector index. Your agent searches through past task history to answer questions about firm productivity. This turns static API data into a queryable memory. You ask about project status and get answers grounded in real-time account records.

Ground LlamaIndex answers in API facts

Stop guessing what your firm is doing. The agent calls `list_firm_team_members` to verify who is assigned to a specific project before answering your query. Accuracy improves because the agent references live data. It pulls the latest figures from `list_time_tracking_logs` rather than relying on stale training weights.

Filter Aero Workflow access

Use the allowed_tools filter to control what your LlamaIndex agent can touch. You define which operations are available for the search index. This keeps your accounting data secure. Your agent only performs the actions you explicitly permit when querying the index.

Setup guide

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

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Common questions about Aero Workflow MCP in LlamaIndex

Use McpToolSpec to wrap the server connection. Once you convert the tools to a list, they become available for your FunctionAgent to query.

Yes, you can feed tool outputs into your vector store. This allows you to perform semantic queries across your firm's entire workflow history.

Absolutely, integrate live API data alongside your documents. The agent pulls active tasks and combines them with your internal policy files for complete answers.

Yes, use the allowed_tools filter during setup. This ensures your agent only uses the tools necessary for your specific RAG application.

The server uses endpoint token authentication for every request. Your firm's customer names and contact info are never cached or exposed outside your environment.

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