Jobtoolz MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Jobtoolz as an MCP tool provider through the 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 Jobtoolz. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Jobtoolz?"
)
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 Jobtoolz MCP Server
Empower your AI agents with Jobtoolz's recruitment management platform. This MCP server allows you to list jobs, track candidates, manage pipeline stages, and view departments and locations directly through the Jobtoolz API. Ideal for automating hiring workflows and candidate engagement.
LlamaIndex agents combine Jobtoolz tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through the Vinkius and query live data alongside vector stores and SQL databases in a single turn — ideal for hybrid search, data enrichment, and analytical workflows.
The Jobtoolz MCP Server exposes 10 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 Jobtoolz to LlamaIndex via MCP
Follow these steps to integrate the Jobtoolz 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 10 tools from Jobtoolz
Why Use LlamaIndex with the Jobtoolz MCP Server
LlamaIndex provides unique advantages when paired with Jobtoolz through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Jobtoolz tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Jobtoolz tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Jobtoolz, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Jobtoolz tools were called, what data was returned, and how it influenced the final answer
Jobtoolz + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Jobtoolz MCP Server delivers measurable value.
Hybrid search: combine Jobtoolz real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Jobtoolz 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 Jobtoolz for fresh data
Analytical workflows: chain Jobtoolz queries with LlamaIndex's data connectors to build multi-source analytical reports
Jobtoolz MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Jobtoolz to LlamaIndex via MCP:
get_candidate
Returns contact details, application history, and custom field values. Use this for deep-dive vetting of an applicant. Retrieves details for a specific candidate
get_job
Returns descriptions, requirements, and internal status. Essential for detailed analysis of a specific role. Retrieves details for a specific job
list_candidates
Includes candidate names, IDs, and current pipeline status. Use this to monitor applicant flow and identify recent entries. Lists all candidates
list_departments
Useful for filtering jobs and candidates by business unit (e.g., Sales, R&D). Lists all departments
list_jobs
Returns job titles, IDs, and departments. Use this to identify open positions and locate job IDs for candidate management. Lists all active jobs
list_locations
Useful for identifying jobs in specific geographical regions. Lists all office locations
list_sources
g., "Company Website", "Indeed") configured in Jobtoolz. Useful for auditing the origins of candidate traffic. Lists all recruitment sources
list_stages
g., "Applied", "Interview", "Offer"). Essential for understanding the company's hiring process. Lists all configured pipeline stages
list_tags
Useful for identifying valid tags before performing a tagged search. Lists all configured tags
list_users
Useful for identifying account administrators or hiring managers. Lists all organization users
Example Prompts for Jobtoolz in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Jobtoolz immediately.
"List all open jobs in Jobtoolz."
"Show me the details for candidate ID '123'."
"Check the available recruitment sources."
Troubleshooting Jobtoolz MCP Server with LlamaIndex
Common issues when connecting Jobtoolz to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpJobtoolz + LlamaIndex FAQ
Common questions about integrating Jobtoolz 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 Jobtoolz with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
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GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
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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 Jobtoolz to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.