Wizehire MCP Server for LlamaIndexGive LlamaIndex instant access to 12 tools to Check Api Health, Create New Candidate, Get Candidate Details, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Wizehire as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
Ask AI about this App Connector for LlamaIndex
The Wizehire app connector for LlamaIndex is a standout in the Industry Titans category — giving your AI agent 12 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Wizehire. "
"You have 12 tools available."
),
)
response = await agent.run(
"What tools are available in Wizehire?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Wizehire MCP Server
Connect your Wizehire hiring platform to any AI agent to streamline your recruitment lifecycle and candidate discovery. Wizehire provides a comprehensive ATS for managing applicant pipelines and assessments.
LlamaIndex agents combine Wizehire tool responses with indexed documents for comprehensive, grounded answers. Connect 12 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Candidate Orchestration — List applicants and retrieve detailed contact profiles with DISC+ assessment data.
- Job Oversight — Monitor active job postings and retrieve technical requirements and descriptions directly.
- Pipeline Automation — Move candidates between hiring stages like Interview or Hired via natural conversation.
- Team Management — List hiring team members and manage available recruitment stages programmatically.
- Workflow Intelligence — Get a comprehensive overview of your active hiring pipelines using natural language.
The Wizehire MCP Server exposes 12 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
All 12 Wizehire tools available for LlamaIndex
When LlamaIndex connects to Wizehire through Vinkius, your AI agent gets direct access to every tool listed below — spanning hiring-platform, candidate-tracking, job-postings, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Verify Wizehire API connectivity
Requires name and email. Add a new candidate manually
Get details for a specific candidate
Get authenticated user profile
Get details for a specific job
List all active job openings
List all recruitment candidates
List active webhooks
List defined hiring stages
List hiring managers and team members
List business office locations
g., Interview, Hired). Move a candidate to a different stage
Connect Wizehire to LlamaIndex via MCP
Follow these steps to wire Wizehire into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the Wizehire MCP Server
LlamaIndex provides unique advantages when paired with Wizehire through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Wizehire tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Wizehire tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Wizehire, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Wizehire tools were called, what data was returned, and how it influenced the final answer
Wizehire + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Wizehire MCP Server delivers measurable value.
Hybrid search: combine Wizehire real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Wizehire to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Wizehire for fresh data
Analytical workflows: chain Wizehire queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for Wizehire in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Wizehire immediately.
"List my active job postings in Wizehire."
"Show the latest candidates for the 'Sales Executive' role."
Troubleshooting Wizehire MCP Server with LlamaIndex
Common issues when connecting Wizehire to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpWizehire + LlamaIndex FAQ
Common questions about integrating Wizehire MCP Server with LlamaIndex.