How to Use the intelliHR MCP in LlamaIndex

Q: Can I use LlamaIndex to query intelliHR remuneration details?

Yes, you can expose listremuneration to your query engine. The engine indexes the salary data so you can ask natural language questions about pay ranges. This keeps your financial reporting conversational and fast.

Index live intelliHR employee records and training data directly into your LlamaIndex vector stores for RAG.

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Connect intelliHR MCP to LlamaIndex

Create your Vinkius account to connect intelliHR 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 intelliHR with LlamaIndex

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Index Employee Records for Semantic Search

The `list_people` tool retrieves complete profile records that your pipeline indexes directly into a vector database. This allows you to run semantic search queries over your workforce directory instead of relying on rigid database filters. Your LlamaIndex agent queries this index to find specific individuals based on natural language descriptions. Because the data is grounded in actual API outputs, your agent avoids making up details about job titles or reporting lines.

Build RAG Pipelines for Corporate Training

Your system calls `list_training` to fetch historical learning and development records across the entire company. LlamaIndex ingests these records, letting you build search tools that answer complex questions about team qualifications and completed courses. If a manager asks who is qualified for a new project, the RAG pipeline searches the indexed training logs. It matches the requirements against actual completed courses, giving you an accurate list of eligible staff.

Analyze Organization Structures with an MCP Server

The `list_business_entities` tool lists your legal corporate entities, which you can combine with office data from `list_locations` in your index. This gives your query engine a clear, structured view of your physical and legal setup. You can filter these resources dynamically during retrieval to keep your context windows small and relevant. Connecting this MCP Server ensures your index always reflects real-world office assignments and entity changes.

Setup guide

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

You load the tools into your environment and wrap them using the tool spec adapter. When your pipeline calls `list_positions`, the returned JSON is converted into document nodes. LlamaIndex then indexes these nodes directly into your vector store.

Yes, you can expose `list_remuneration` to your query engine. The engine indexes the salary data so you can ask natural language questions about pay ranges. This keeps your financial reporting conversational and fast.

You need to set up a polling schedule to keep your index current. Every time your script runs, it calls `list_jobs` to check for new roles and updates the index. This ensures your search results reflect recent hires and promotions.

You can pass a list of allowed tools when initializing your MCP client. If you only want to index skills, just allow `list_skills` and ignore the rest. This prevents your indexer from pulling unnecessary organizational details.

We route all tool traffic through a zero-trust architecture where credentials are never exposed to the LLM. Your training records and defined skills are processed entirely in memory. No data is stored on our platform, keeping your corporate development records completely private.

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