ApplicantStack MCP Server for LlamaIndex 7 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add ApplicantStack 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 MCP SERVER
Vinkius supports streamable HTTP and SSE.
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 ApplicantStack. "
"You have 7 tools available."
),
)
response = await agent.run(
"What tools are available in ApplicantStack?"
)
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 ApplicantStack MCP Server
The ApplicantStack MCP Server integrates your recruiting and onboarding workflows directly into your AI workspace. Efficiently manage your job listings, track candidate progress through custom stages, and streamline your hiring process using simple natural language.
LlamaIndex agents combine ApplicantStack tool responses with indexed documents for comprehensive, grounded answers. Connect 7 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.
Key Features
- Job Management — List all active and closed job openings, and retrieve full metadata for any specific listing.
- Candidate Tracking — Access your entire applicant database and filter by workflow stage or score.
- Workflow Automation — Move candidates between stages (e.g., from 'Interview' to 'Hired') and update their profiles instantly.
- Onboarding & Hires — Access onboarding data for new hires to ensure a smooth transition from applicant to employee.
- Secure Access — Uses private access tokens to safely interact with your organization's recruiting data.
Benefits for Teams
- Recruiters — Quickly check the status of candidates for multiple jobs without switching between tabs.
- Hiring Managers — Review candidate profiles and scores using AI-assisted summaries.
- HR Teams — Track hiring trends and ensure onboarding tasks are initiated for all new hires.
The ApplicantStack MCP Server exposes 7 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.
How to Connect ApplicantStack to LlamaIndex via MCP
Follow these steps to integrate the ApplicantStack MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 7 tools from ApplicantStack
Why Use LlamaIndex with the ApplicantStack MCP Server
LlamaIndex provides unique advantages when paired with ApplicantStack through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine ApplicantStack tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain ApplicantStack tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query ApplicantStack, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what ApplicantStack tools were called, what data was returned, and how it influenced the final answer
ApplicantStack + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the ApplicantStack MCP Server delivers measurable value.
Hybrid search: combine ApplicantStack real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query ApplicantStack 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 ApplicantStack for fresh data
Analytical workflows: chain ApplicantStack queries with LlamaIndex's data connectors to build multi-source analytical reports
ApplicantStack MCP Tools for LlamaIndex (7)
These 7 tools become available when you connect ApplicantStack to LlamaIndex via MCP:
get_account_check
Verify ApplicantStack account connection
get_candidate
Get details for a specific candidate
get_job
Get details for a specific job
list_candidates
List all candidates
list_hires
List all hires (onboarding)
list_jobs
List all job listings in ApplicantStack
update_candidate
Use stage field to move them in the workflow. Update candidate information or stage
Example Prompts for ApplicantStack in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with ApplicantStack immediately.
"List all active job openings in ApplicantStack."
"Show me candidates currently in the 'Interview' stage."
"Move candidate 'C12345' to the 'Hired' stage."
Troubleshooting ApplicantStack MCP Server with LlamaIndex
Common issues when connecting ApplicantStack to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpApplicantStack + LlamaIndex FAQ
Common questions about integrating ApplicantStack MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect ApplicantStack with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect ApplicantStack to LlamaIndex
Get your token, paste the configuration, and start using 7 tools in under 2 minutes. No API key management needed.