Join 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 Join as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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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 Join. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Join?"
)
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 Join MCP Server
Empower your AI agents with JOIN's modern recruiting platform. This MCP server allows you to list job openings, retrieve candidate details, manage applications, and view organization departments directly through the JOIN API. Ideal for automating hiring workflows and talent acquisition.
LlamaIndex agents combine Join tool responses with indexed documents for comprehensive, grounded answers. Connect 10 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.
The Join 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 Join to LlamaIndex via MCP
Follow these steps to integrate the Join 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 Join
Why Use LlamaIndex with the Join MCP Server
LlamaIndex provides unique advantages when paired with Join through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Join tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Join tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Join, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Join tools were called, what data was returned, and how it influenced the final answer
Join + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Join MCP Server delivers measurable value.
Hybrid search: combine Join real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Join 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 Join for fresh data
Analytical workflows: chain Join queries with LlamaIndex's data connectors to build multi-source analytical reports
Join MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Join to LlamaIndex via MCP:
get_application
Returns answers to form questions, internal notes, and application status. Use when evaluating a specific applicant or moving them through the pipeline. Retrieves details for a specific application
get_candidate
Use this for detailed candidate vetting and interview preparation. Retrieves details for a specific candidate
get_job
Returns descriptions, requirements, and internal metadata. Use this when the user needs to analyze the specifics of a particular role or prepare content related to it. Retrieves details for a specific job
get_me
Use this to verify identity and check connection health. Gets details about your own authenticated user
list_applications
Includes candidate summaries and basic application info. Essential for monitoring recent applicant flow and identifying new leads in the recruitment process. Lists all job applications
list_candidates
Returns candidate profiles, contact info, and their association with jobs. Use this when the user wants to search for specific people or perform bulk talent management tasks. Lists all candidates in the system
list_departments
g., Engineering, Sales, HR). Useful for filtering jobs or organizing the recruiting workspace by functional areas. Lists all organization departments
list_jobs
Returns job titles, IDs, and current status. Use this as the primary entry point to identify specific jobs or to provide an overview of the current hiring pipeline. Lists all job postings in JOIN
list_locations
Use this when the user asks for jobs in specific regions or needs to audit location-based recruiting data. Lists all job locations
list_users
Useful for identifying hiring managers or checking account access permissions. Lists all users in your JOIN account
Example Prompts for Join in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Join immediately.
"List all active job postings in JOIN."
"Show me the latest candidate applications."
"Get details for candidate ID '123'."
Troubleshooting Join MCP Server with LlamaIndex
Common issues when connecting Join to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpJoin + LlamaIndex FAQ
Common questions about integrating Join 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 Join 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 Join to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.