How to Use the Armano MCP in LlamaIndex

Q: How do I connect LlamaIndex to the Armano MCP Server?

First, install llama-index-tools-mcp and instantiate the BasicMCPClient with your Vinkius URL. Then, wrap it in a McpToolSpec and call totoollistasync() to get tools for your FunctionAgent.

Q: Can LlamaIndex agents update timesheets on Armano?

Yes, you expose the createtimeentry tool to your LlamaIndex FunctionAgent. The agent calls this tool to write new records when users submit hours through your chat interface.

Q: How does LlamaIndex handle semantic search over Armano projects?

LlamaIndex fetches project details using getproject and converts the JSON output into text nodes. These nodes are then embedded and stored in your vector database for fast semantic retrieval.

Q: Can I limit LlamaIndex to read-only tools on this MCP Server?

Yes, you use the allowedtools filter when setting up your McpToolSpec. This lets you expose safe tools like listprojects while blocking tools that modify data.

Index Armano project and time data into LlamaIndex to build RAG applications grounded in real-world business metrics.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Armano MCP to LlamaIndex

Create your Vinkius account to connect Armano 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.

GDPR Free for Subscribers

Setup Armano with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Index Armano MCP Server data for semantic search

LlamaIndex takes the outputs of `list_time_entries` and indexes them directly into your vector database. This lets you run semantic queries across historical timesheets instead of writing complex SQL queries. Your queries are grounded in actual data returned by `get_project`. The index stores these records so your RAG pipeline instantly retrieves accurate project details without hallucinating numbers.

Query employee profiles and leave requests

By indexing the output of `list_employees` and `list_leaves`, LlamaIndex builds a searchable knowledge base of your workforce. Your agent queries this index to find who is available for new assignments. The framework combines the structured data from `get_employee` with unstructured documents. This lets your agent cross-reference internal resumes with actual logged hours to find the right specialist.

Build live operational RAG pipelines

LlamaIndex queries the active Armano API on the fly using `check_armano_status` to ensure your index is fresh. It pulls the latest department structures with `list_departments` before running a query. Your RAG application always reflects the current state of projects because the MCP Server outputs are indexed in real time.

Setup guide

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Armano. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Armano now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Armano MCP in LlamaIndex

First, install `llama-index-tools-mcp` and instantiate the `BasicMCPClient` with your Vinkius URL. Then, wrap it in a `McpToolSpec` and call `to_tool_list_async()` to get tools for your `FunctionAgent`.

Yes, you expose the `create_time_entry` tool to your LlamaIndex `FunctionAgent`. The agent calls this tool to write new records when users submit hours through your chat interface.

LlamaIndex fetches project details using `get_project` and converts the JSON output into text nodes. These nodes are then embedded and stored in your vector database for fast semantic retrieval.

Yes, you use the `allowed_tools` filter when setting up your `McpToolSpec`. This lets you expose safe tools like `list_projects` while blocking tools that modify data.

Your timesheet and project records are processed in a zero-trust, ephemeral environment. Vinkius hosts the MCP Server securely, ensuring no sensitive business metrics are cached or leaked.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript