How to Use the Avaza MCP in LlamaIndex

Q: Can LlamaIndex search through my Avaza timesheets?

It pulls the data first. You call listtimesheets, index the JSON response into your vector store, and then query it like any other document.

Turn your Avaza project and billing data into a searchable vector index with LlamaIndex.

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Connect Avaza MCP to LlamaIndex

Create your Vinkius account to connect Avaza 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 Avaza with LlamaIndex

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Index Avaza Data with LlamaIndex

RAG applications need actual ground truth to stop hallucinations. You feed `list_projects` and `get_project` outputs from the MCP Server directly into your vector store. The model now knows exactly what your team is working on. Querying project status becomes a simple semantic search. Your agent pulls context from `list_tasks` to answer questions about deadlines. It synthesizes live API responses with your internal documentation.

Build Billing RAG Pipelines

Financial queries require exact numbers. By indexing the output of `list_invoices`, your agent can answer questions about monthly revenue without guessing. It maps invoice line items to specific client records. Time tracking data adds another layer of context. The MCP Server lets your agent run `list_timesheets` to compare billed hours against project budgets. You get instant answers backed by actual database entries.

Stateful Contact Management

Finding the right person usually means digging through directories. Your setup indexes the results of `list_contacts` so the agent knows who belongs to which company. Ask it for the lead developer at a specific client, and it knows immediately. The system also takes action based on retrieved context. After finding the right contact and project, it can fire `update_task` to reassign work. You bridge the gap between static knowledge and active operations.

Setup guide

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

Install llama-index-tools-mcp via pip. Initialize a BasicMCPClient with your Vinkius URL and wrap it in McpToolSpec.

It pulls the data first. You call list_timesheets, index the JSON response into your vector store, and then query it like any other document.

Vinkius manages the OAuth flow. You just provide the single endpoint token to your client setup, and the server handles the underlying API headers.

You filter the tool list before passing it to FunctionAgent. Just exclude create_project or create_timesheet if you want a read-only index.

Vinkius runs your server in an ephemeral V8 isolate. When your script pulls list_contacts, the data passes through a zero-trust tunnel directly to your local LlamaIndex instance, leaving no cache behind.

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