How to Use the Unanet MCP in LlamaIndex

Q: How does LlamaIndex improve querying of Unanet project data?

LlamaIndex indexes the output from projects into a vector store. This means if you ask an open-ended question about project scope, it pulls relevant details rather than just returning a list.

Q: Can I use LlamaIndex to build a knowledge base around Unanet user data?

Yes. By indexing the output of users, you create a persistent knowledge source. You can query past sessions or configurations and get answers grounded in actual API data.

Q: What is the best way to combine Unanet timesheet data with LlamaIndex?

You index the output of timesheets. This lets you build a comprehensive knowledge base that tracks not just hours, but context around why those hours were logged in previous queries.

Q: Does this MCP Server support advanced data filtering with LlamaIndex?

Yes. The setup supports using allowedtools filters, letting you specify exactly which tools and resources the agent can access when building your RAG app.

Q: What kind of data does this MCP Server touch regarding financial records?

The server exposes expense reporting through the expenses tool, touching on specific user spending details. This is indexed as a searchable knowledge type for retrieval.

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Connect Unanet MCP to LlamaIndex

Create your Vinkius account to connect Unanet 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 Unanet with LlamaIndex

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LlamaIndex: Indexing Unanet data into vector stores

When you query `projects`, the tool output becomes part of a searchable index. Instead of just getting a list, LlamaIndex helps you find all historical context related to that project. This means your agent can answer complex questions like, 'What were the top expenses for Project X last quarter?' using grounded data from the MCP Server.

Searching user roles with `users`

You use the `users` tool to fetch employee records. LlamaIndex takes these results and indexes them semantically. This lets you query configurations—like 'Find employees who handle billing for department Y'—without knowing the exact field name. It’s about asking questions, not running specific API calls.

Retrieving expense history using `expenses`

`expenses` lists reports for a user, but LlamaIndex makes that data searchable. If you query 'Why was the travel budget high last year?', it pulls relevant historical expense records from Unanet. This allows your RAG application to combine live API data with document context for better answers.

Setup guide

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

LlamaIndex indexes the output from `projects` into a vector store. This means if you ask an open-ended question about project scope, it pulls relevant details rather than just returning a list.

Yes. By indexing the output of `users`, you create a persistent knowledge source. You can query past sessions or configurations and get answers grounded in actual API data.

You index the output of `timesheets`. This lets you build a comprehensive knowledge base that tracks not just hours, but context around why those hours were logged in previous queries.

Yes. The setup supports using `allowed_tools` filters, letting you specify exactly which tools and resources the agent can access when building your RAG app.

The server exposes expense reporting through the `expenses` tool, touching on specific user spending details. This is indexed as a searchable knowledge type for retrieval.

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