How to Use the Moka HR MCP in LlamaIndex

Q: How do I index Moka HR data into LlamaIndex?

You use the llama-index-tools-mcp package to initialize the basic MCP client. Once connected, you convert tools like getcandidate into a tool list and pass them to your RAG agent.

Q: Can I search through Moka HR resumes using LlamaIndex?

Yes, you can use listcandidates to pull applicant data and load it into a vector index. This lets you run natural language queries over your entire pool of candidate profiles instead of relying on basic filters.

Q: How does this setup protect sensitive candidate information?

All candidate data retrieved via getcandidate is processed in memory and never cached on external servers. The Vinkius sandbox ensures your recruitment data remains isolated, keeping sensitive applicant details fully protected.

Index live Moka HR candidate profiles and job descriptions into your LlamaIndex vector stores for semantic search.

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Connect Moka HR MCP to LlamaIndex

Create your Vinkius account to connect Moka HR 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 Moka HR with LlamaIndex

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Semantic Search Over Candidate Profiles

The `get_candidate` tool lets you fetch detailed candidate records and load them directly into your LlamaIndex document store. Instead of relying on rigid keyword matching, your agent can run semantic queries to find candidates with relevant experience. This approach turns raw ATS data into an active knowledge base. You can ask your agent to find past applicants who have specific engineering experience, and it will pull the right profiles instantly.

Build RAG Pipelines with the MCP Server

You can use `get_hiring_summary` to retrieve concise performance metrics and candidate evaluations for your retrieval-augmented generation pipelines. LlamaIndex indexes these summaries so your agent can answer complex questions about your current hiring pipeline. This setup eliminates the need to manually read through dozens of applications. Your agent queries the index, matches candidate profiles with open roles, and summarizes the best fits for your team.

Match Jobs to Applicants Automatically

The `list_jobs` tool retrieves all open positions so your agent can map them against incoming candidate profiles. LlamaIndex parses the job requirements and compares them to candidates fetched via `list_candidates` to find the best matches. By indexing both sides of your hiring pipeline, you get a system that suggests candidates for new roles the moment they open. You don't have to manually search your database every time a hiring manager submits a request.

Setup guide

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

You use the `llama-index-tools-mcp` package to initialize the basic MCP client. Once connected, you convert tools like `get_candidate` into a tool list and pass them to your RAG agent.

Yes, you can use `list_candidates` to pull applicant data and load it into a vector index. This lets you run natural language queries over your entire pool of candidate profiles instead of relying on basic filters.

The server fetches live data whenever your agent calls a tool like `get_application`. To keep your vector store updated, you can set up a periodic ingestion pipeline that queries the server and refreshes your index.

Yes, you can specify an allowed tools list when setting up your MCP tool spec. This lets you restrict your agent to read-only tools like `list_jobs` if you want to prevent it from modifying candidate data.

All candidate data retrieved via `get_candidate` is processed in memory and never cached on external servers. The Vinkius sandbox ensures your recruitment data remains isolated, keeping sensitive applicant details fully protected.

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