How to Use the Lucca (HR & Finance Suite) MCP in LlamaIndex

Q: How do I connect Lucca (HR & Finance Suite) to my LlamaIndex pipeline?

Install llama-index-tools-mcp and initialize the basic client pointing to your Vinkius gateway. Convert the server tools to a list using McpToolSpec and pass them to your agent.

Q: Can I index Lucca (HR & Finance Suite) expense reports for semantic search in LlamaIndex?

Yes, you can load data from listexpensereports into a vector store using LlamaIndex document parsers. This lets you run semantic queries over Cleemy financial records.

Q: How does LlamaIndex handle live data from Lucca (HR & Finance Suite) instead of static files?

The framework calls tools like getleavebalances dynamically during query execution when using a function agent. This ensures your answers reflect active Timmi balances rather than cached documents.

Q: Can I filter which Lucca tools are exposed to the agent?

Yes, you can use the allowedtools list when configuring your tool spec. This lets you restrict access to sensitive endpoints like getuser while allowing access to department lists.

Index Lucca HR data directly into LlamaIndex vector stores to build semantic search engines for expense and leave history.

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Connect Lucca (HR & Finance Suite) MCP to LlamaIndex

Create your Vinkius account to connect Lucca (HR & Finance Suite) 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 Lucca (HR & Finance Suite) with LlamaIndex

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Index Lucca timesheets and leave records in LlamaIndex

Ground your LlamaIndex RAG applications in actual company data by indexing Lucca outputs from `list_timesheets` and `list_leaves`. LlamaIndex converts these raw Lucca API payloads into searchable document nodes, eliminating hallucinations about who is out of the office. Your LlamaIndex agent queries this vector store to answer complex scheduling questions from Timmi instantly. Instead of parsing raw JSON, LlamaIndex gives you natural language summaries backed by real-time Timmi data.

Semantic search over corporate expense reports

Feed data from `list_expense_reports` and `list_expense_claims` directly into your LlamaIndex pipelines. The LlamaIndex framework structures these Lucca financial records so your LLM can detect patterns, like recurring subscription costs or travel anomalies. This MCP setup combines live Lucca API lookups with historical LlamaIndex data stores. You can enforce strict document access controls in LlamaIndex while querying sensitive Cleemy files.

Dynamic organization mapping for query routing

Query organizational structures using `list_departments` and `list_users` to route natural language questions to the right LlamaIndex data index. If a LlamaIndex user asks about engineering budgets, the router maps the Lucca department ID before querying financial records. Using this MCP Server ensures your LlamaIndex index stays updated with fresh Lucca data without manual exports. The LlamaIndex agent pulls fresh Lucca data through the tool spec whenever a query requires real-time accuracy.

Setup guide

Set up Lucca (HR & Finance Suite) 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 Lucca (HR & Finance Suite) 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 Lucca (HR & Finance Suite) tools.",
)
response = await agent.run("List recent Lucca (HR & Finance Suite) data")

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Common questions about Lucca (HR & Finance Suite) MCP in LlamaIndex

Install `llama-index-tools-mcp` and initialize the basic client pointing to your Vinkius gateway. Convert the server tools to a list using `McpToolSpec` and pass them to your agent.

Yes, you can load data from `list_expense_reports` into a vector store using LlamaIndex document parsers. This lets you run semantic queries over Cleemy financial records.

The framework calls tools like `get_leave_balances` dynamically during query execution when using a function agent. This ensures your answers reflect active Timmi balances rather than cached documents.

Yes, you can use the `allowed_tools` list when configuring your tool spec. This lets you restrict access to sensitive endpoints like `get_user` while allowing access to department lists.

Your raw employee profiles and leave records are processed locally in memory before indexing. The MCP Server communicates over an encrypted single-token endpoint managed by Vinkius, protecting sensitive personal data from exposure.

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