CATS ATS MCP Server for LlamaIndex 8 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add CATS ATS 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 CATS ATS. "
"You have 8 tools available."
),
)
response = await agent.run(
"What tools are available in CATS ATS?"
)
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 CATS ATS MCP Server
Connect your CATS Applicant Tracking System (ATS) account to any AI agent and take full control of your recruitment pipeline through natural conversation. Streamline candidate sourcing and job management.
LlamaIndex agents combine CATS ATS tool responses with indexed documents for comprehensive, grounded answers. Connect 8 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.
What you can do
- Candidate Discovery — Search and list candidate profiles with detailed contact information and status natively
- Job Oversight — List and retrieve details for all active job orders and open positions flawlessly
- Activity Tracking — Monitor recent recruitment activities, notes, and interactions securely
- Organizational Mapping — List client companies and associated contacts within your ATS flawlessly
- HAL-native Integration — Access deep object relationships through the CATS v3 HAL API structure in real-time
- Pipeline Analysis — Audit candidate progress across different recruitment stages directly within your workspace
The CATS ATS MCP Server exposes 8 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 CATS ATS to LlamaIndex via MCP
Follow these steps to integrate the CATS ATS 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 8 tools from CATS ATS
Why Use LlamaIndex with the CATS ATS MCP Server
LlamaIndex provides unique advantages when paired with CATS ATS through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine CATS ATS tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain CATS ATS tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query CATS ATS, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what CATS ATS tools were called, what data was returned, and how it influenced the final answer
CATS ATS + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the CATS ATS MCP Server delivers measurable value.
Hybrid search: combine CATS ATS real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query CATS ATS 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 CATS ATS for fresh data
Analytical workflows: chain CATS ATS queries with LlamaIndex's data connectors to build multi-source analytical reports
CATS ATS MCP Tools for LlamaIndex (8)
These 8 tools become available when you connect CATS ATS to LlamaIndex via MCP:
get_candidate_details
Get detailed information for a specific candidate
get_job_details
Get detailed information for a specific job order
list_candidates
List recruitment candidates
list_job_orders
List job orders and open positions
list_recruitment_activities
List recent recruitment activities and notes
list_recruitment_companies
List client companies in the ATS
list_recruitment_contacts
List contacts associated with companies
search_candidates
Search for candidates using filters
Example Prompts for CATS ATS in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with CATS ATS immediately.
"Search for candidates named 'Sarah' in my CATS ATS."
"What are the active job orders right now?"
"Show me the last 10 recruitment activities."
Troubleshooting CATS ATS MCP Server with LlamaIndex
Common issues when connecting CATS ATS to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpCATS ATS + LlamaIndex FAQ
Common questions about integrating CATS ATS 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 CATS ATS 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 CATS ATS to LlamaIndex
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.