Jobtoolz MCP Server for LangChain 10 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Jobtoolz through the Vinkius and LangChain agents can call every tool natively — combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
async with MultiServerMCPClient({
"jobtoolz": {
"transport": "streamable_http",
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
}
}) as client:
tools = client.get_tools()
agent = create_react_agent(
ChatOpenAI(model="gpt-4o"),
tools,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using Jobtoolz, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
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.
LangChain's ecosystem of 500+ components combines seamlessly with Jobtoolz through native MCP adapters. Connect 10 tools via the Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures — with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
The Jobtoolz MCP Server exposes 10 tools through the Vinkius. Connect it to LangChain 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 LangChain via MCP
Follow these steps to integrate the Jobtoolz MCP Server with LangChain.
Install dependencies
Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save the code and run python agent.py
Explore tools
The agent discovers 10 tools from Jobtoolz via MCP
Why Use LangChain with the Jobtoolz MCP Server
LangChain provides unique advantages when paired with Jobtoolz through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents — combine Jobtoolz MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across Jobtoolz queries for multi-turn workflows
Jobtoolz + LangChain Use Cases
Practical scenarios where LangChain combined with the Jobtoolz MCP Server delivers measurable value.
RAG with live data: combine Jobtoolz tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Jobtoolz, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Jobtoolz tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Jobtoolz tool call, measure latency, and optimize your agent's performance
Jobtoolz MCP Tools for LangChain (10)
These 10 tools become available when you connect Jobtoolz to LangChain 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 LangChain
Ready-to-use prompts you can give your LangChain 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 LangChain
Common issues when connecting Jobtoolz to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersJobtoolz + LangChain FAQ
Common questions about integrating Jobtoolz MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
Connect Jobtoolz 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 Jobtoolz to LangChain
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.