How to Use the Humaans MCP in LlamaIndex

Index your Humaans HR data into LlamaIndex to create queryable knowledge bases for your internal teams.

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Connect Humaans MCP to LlamaIndex

Create your Vinkius account to connect Humaans 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 Humaans with LlamaIndex

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Index Humaans data for LlamaIndex

Convert your employee directory into a searchable vector index by pulling results from `list_employees`. LlamaIndex takes this output and grounds your AI in your actual company structure. Stop relying on stale documents. Use `list_teams` and `list_departments` to ensure your RAG application knows exactly who is in which group and what their current role is.

Query HR documents with LlamaIndex

Point your agent at `list_documents` to ingest company policies and employee-specific files. Once indexed, your LlamaIndex application can answer questions about HR status by referencing the raw data. This approach turns static HR records into dynamic answers. You'll find that `get_employee` provides the specific context needed for highly personalized document retrieval.

Track time off via LlamaIndex

Feed `list_leaves` into your knowledge base to monitor company-wide availability. LlamaIndex processes these records so you can ask natural language questions about upcoming time off. It works by combining the list of holidays from `list_public_holidays` with active leave requests. Your agent now understands your organization's calendar as well as any HR administrator.

Setup guide

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

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Common questions about Humaans MCP in LlamaIndex

You use the MCP tool spec to fetch the tool definitions and pass them to your agent. The agent then executes the tools and indexes the results into your vector store.

Once the tools are connected, LlamaIndex can pull any requested HR data and store it as embeddings. You can then perform semantic searches across your entire employee database.

Because the tools are live, you can re-index or query the API again to refresh your knowledge base. Your LlamaIndex application stays as current as your HRIS records.

No, the tool spec automatically handles the schema conversion. You map the tools to your agents, and the MCP protocol manages the communication.

The server transmits only the data you authorize for your index. Your Humaans employee records and leave history are processed through an encrypted, ephemeral tunnel managed by Vinkius.

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