How to Use the ApplicantStack MCP in LlamaIndex

Q: How do I expose ApplicantStack MCP Server tools to LlamaIndex?

Install llama-index-tools-mcp via pip. Create a BasicMCPClient pointing to your Vinkius URL, wrap it in an McpToolSpec, and pass the async tool list to your FunctionAgent.

Q: Can LlamaIndex search across all my ApplicantStack candidates?

Your agent calls listcandidates and getcandidate to ingest applicant records. LlamaIndex then embeds this data into your vector store, allowing semantic queries across the entire candidate pool.

Q: How does LlamaIndex know which ApplicantStack job a candidate applied for?

The agent pulls specific role details using the getjob tool. It indexes the job requirements alongside the candidate profile, letting you query the relationship between the two.

Q: Can I move candidates between stages using this integration?

The updatecandidate tool handles stage changes. You configure your agent to accept stage movement commands from your recruiters during their chat sessions.

Index ApplicantStack candidate data into LlamaIndex to build searchable RAG applications for your hiring team.

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Connect ApplicantStack MCP to LlamaIndex

Create your Vinkius account to connect ApplicantStack 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 ApplicantStack with LlamaIndex

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Index recruitment data via the MCP Server

This ApplicantStack MCP Server provides seven tools for extracting and modifying applicant tracking records inside LlamaIndex workflows. You query `list_candidates` and `list_jobs` to pull live hiring data directly into your vector store for semantic search. Your RAG application doesn't just guess at candidate qualifications. It runs `get_candidate` to fetch exact resume details and interview notes, indexing the text so your hiring managers can query the actual applicant pool.

Ground hiring decisions in live API data

LlamaIndex agents cross-reference open roles against applicant profiles by calling `get_job` and comparing the requirements to indexed candidate documents. You build systems that answer questions based on real applicant data rather than outdated spreadsheets. When a hiring manager asks about recent activity, the agent executes `list_hires` to pull the latest onboarding records. The tool output becomes part of the searchable knowledge base, grounding the agent's response in live system state.

Update candidate status from chat

Your LlamaIndex `FunctionAgent` can push data back to the ATS using the `update_candidate` tool. After a recruiter queries the vector store for a specific applicant and reviews their indexed profile, they can tell the agent to advance the candidate's stage. You maintain system reliability by calling `get_account_check` before executing write operations. The agent confirms the connection is active, updates the stage, and then refreshes the vector index with the new candidate status.

Setup guide

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

Install `llama-index-tools-mcp` via pip. Create a `BasicMCPClient` pointing to your Vinkius URL, wrap it in an `McpToolSpec`, and pass the async tool list to your `FunctionAgent`.

Your agent calls `list_candidates` and `get_candidate` to ingest applicant records. LlamaIndex then embeds this data into your vector store, allowing semantic queries across the entire candidate pool.

The agent pulls specific role details using the `get_job` tool. It indexes the job requirements alongside the candidate profile, letting you query the relationship between the two.

The `update_candidate` tool handles stage changes. You configure your agent to accept stage movement commands from your recruiters during their chat sessions.

The system routes resumes, contact info, and interview notes through a zero-trust architecture. Vinkius authenticates the single endpoint token and runs the tool inside a temporary sandbox that vanishes after the API returns the data.

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