How to Use the HRBlade MCP in LlamaIndex

Q: Can LlamaIndex query HRBlade interview templates?

Yes. By including listinterviews in your tool set, the agent can search through your existing templates to find the right one for a new role.

Q: Does HRBlade allow for semantic search across candidates?

It provides the data. Once the agent calls listcandidates, LlamaIndex indexes the result, allowing you to perform semantic searches across your entire applicant pool.

Q: How do I filter which tools LlamaIndex can access?

Use the allowedtools filter during agent initialization. This restricts the agent to only the specific functions you want it to perform.

Turn your LlamaIndex knowledge base into a living recruitment engine with real-time access to HRBlade tools.

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Connect HRBlade MCP to LlamaIndex

Create your Vinkius account to connect HRBlade 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 HRBlade with LlamaIndex

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Indexing HRBlade data in LlamaIndex

Feed your candidate records directly into your vector store. By calling `list_candidates`, you pull raw data that LlamaIndex converts into searchable knowledge. This grounds your AI in reality. Instead of guessing candidate status, your agent queries the index to find the most up-to-date information fetched from the server.

Grounded interview workflows for LlamaIndex

Build RAG applications that actually act on the data they find. When your agent finds a relevant interview response via `list_interview_responses`, it uses that context to inform future scheduling decisions. This links your documents and live API data. You get an agent that understands your hiring history and executes tasks based on that deep context.

Unified knowledge for LlamaIndex

Combine your internal hiring documents with live HRBlade metrics. The server provides the tools, and LlamaIndex creates a single, queryable interface for your recruitment team. Stop switching between tabs to check candidate progress. Your agent acts as the bridge between your static documentation and the live candidate pipeline.

Setup guide

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

You use the MCP tool spec to wrap the server. This makes every function available to your agents as a callable tool that returns indexed data.

Yes. By including `list_interviews` in your tool set, the agent can search through your existing templates to find the right one for a new role.

It provides the data. Once the agent calls `list_candidates`, LlamaIndex indexes the result, allowing you to perform semantic searches across your entire applicant pool.

Use the allowed_tools filter during agent initialization. This restricts the agent to only the specific functions you want it to perform.

The server handles all authentication. Data indexed by the agent is kept within your local vector store, keeping candidate names and contact info off public servers.

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