How to Use the Namely MCP in LlamaIndex

Index your Namely HRIS data into LlamaIndex for semantic search and grounded answers.

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Connect Namely MCP to LlamaIndex

Create your Vinkius account to connect Namely 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 Namely with LlamaIndex

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Index Namely data with LlamaIndex

Turn your HR records into a searchable knowledge base. By passing the output of `list_profiles` and `list_teams` into your vector store, you make your organization's structure queryable. The agent no longer guesses at employee details. It searches the indexed data first, ensuring that answers about job titles or departments are grounded in the latest Namely records.

Ground LlamaIndex queries in Namely data

Combine live API calls with your existing documents. Your agent uses `get_profile` to verify current information while querying your indexed reports for historical context. This creates a unified view of your organization. You get precise, verifiable answers because the agent reconciles real-time Namely data with your stored knowledge base.

Automate HR lookups in LlamaIndex

Build RAG applications that respond to natural language requests about your company. The agent uses `list_announcements` or `list_events` to provide context-aware responses to user queries. It handles the heavy lifting of data retrieval. You simply define the tools, and the agent decides when to fetch data from Namely to answer the user's question.

Setup guide

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

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Common questions about Namely MCP in LlamaIndex

You use the MCP client to fetch Namely records and index them as tool outputs. LlamaIndex then treats Namely data as part of its searchable knowledge.

Absolutely. Once indexed, your agent performs semantic searches across Namely profiles to find the right person for a specific project or task.

You control the refresh cycle for your index. Your agent queries the Namely API directly when it needs the absolute latest data for a high-priority task.

You can restrict which tools are exposed to the agent. This ensures your LlamaIndex setup only interacts with the Namely data you explicitly authorize.

Security is managed through ephemeral sessions. Namely information is never stored permanently unless you decide to index it, keeping your HR data strictly contained.

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