How to Use the Wizehire MCP in LlamaIndex

Q: What if I want to see all active webhooks with Wizehire and LlamaIndex?

You call listconfiguredwebhooks. Indexing this list lets you search for specific webhook IDs or what they are configured to send data about later on.

Q: Can I query past hiring stages with Wizehire and LlamaIndex?

Yes. By indexing the output of listhiringstages, you can search through historical processes, understanding what stages were used for specific jobs in the past.

Q: Is the API health check useful with Wizehire and LlamaIndex?

Running checkapihealth confirms connectivity before building a query. This ensures your knowledge base is always pointing to live, stable data sources.

Ground your knowledge base with LlamaIndex using the Wizehire MCP Server.

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Connect Wizehire MCP to LlamaIndex

Create your Vinkius account to connect Wizehire 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 Wizehire with LlamaIndex

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Search Job Data in LlamaIndex

When you call `get_job_details`, that output becomes part of your searchable index. Instead of just getting a document, your RAG application can answer questions like, 'What were the requirements for the Senior Developer role last quarter?' LlamaIndex ingests these details into vector stores so you get answers grounded in actual API data, not guesses.

Candidate History in LlamaIndex

Use `list_candidates` to gather a list of people. By indexing this result, you can query past sessions and ask, 'Which candidates were top-tier for engineering roles last year?' The index provides the names and details. It turns transient API calls into persistent, searchable knowledge.

Manage Locations in LlamaIndex

If you need to know where people work or should work, `list_office_locations` pulls that list. You can feed these locations into your index alongside job descriptions. Now, a query about eligibility will hit both the job specs and available offices. This builds a powerful, unified data layer for querying.

Setup guide

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

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Common questions about Wizehire MCP in LlamaIndex

LlamaIndex indexes the results of the MCP Server calls. This means you can query past job openings, candidate details, or webhook configurations and get answers based on actual data.

You call `list_configured_webhooks`. Indexing this list lets you search for specific webhook IDs or what they are configured to send data about later on.

Yes. By indexing the output of `list_hiring_stages`, you can search through historical processes, understanding what stages were used for specific jobs in the past.

The MCP Server works with Candidate records, Job records, and defined Hiring Stage types. All this structured HR information gets indexed for semantic search.

Running `check_api_health` confirms connectivity before building a query. This ensures your knowledge base is always pointing to live, stable data sources.

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