How to Use the CATS ATS MCP in LlamaIndex

Q: How do I set up CATS ATS with LlamaIndex?

Run pip install llama-index-tools-mcp in your environment. Use BasicMCPClient with your Vinkius URL, wrap it in an McpToolSpec, and await the async tool list generation.

Q: Can LlamaIndex filter which CATS ATS MCP tools are active?

You control exactly what the agent sees. Use the allowedtools parameter when initializing the spec to restrict access to safe endpoints like listjoborders.

Index recruitment data into vector stores by connecting the CATS ATS MCP Server to LlamaIndex.

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Connect CATS ATS MCP to LlamaIndex

Create your Vinkius account to connect CATS ATS 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 CATS ATS with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Semantic Candidate Search

`list_candidates` pulls your entire applicant pool into LlamaIndex via MCP for immediate vectorization. The system grabs names, skills, and histories to build a queryable knowledge base. Once indexed, you stop relying on rigid keyword searches. You ask the FunctionAgent for engineers with specific experience, and it retrieves the exact profiles grounded in your actual ATS records.

Client Knowledge Graphs

`list_recruitment_companies` alongside `list_recruitment_contacts` feeds your RAG application with current client relationships. LlamaIndex maps out who works where and what roles they hire for. This turns raw CRM data into an interactive briefing tool. Before a client call, your agent queries the index to summarize past interactions and active job orders without hallucinating details.

Job Order Context via MCP Server

`get_job_details` fetches the deep requirements for specific open roles directly from the ATS. Your script chunks these descriptions and stores them as dense vectors. When a new resume comes in, the system compares it against the indexed job specs. You get automated, mathematically backed matching scores based on live requirements.

Setup guide

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

Run `pip install llama-index-tools-mcp` in your environment. Use `BasicMCPClient` with your Vinkius URL, wrap it in an `McpToolSpec`, and await the async tool list generation.

You control exactly what the agent sees. Use the `allowed_tools` parameter when initializing the spec to restrict access to safe endpoints like `list_job_orders`.

You need to schedule the ingestion runs. Most developers set up a cron job that calls the listing tools nightly to refresh the vector store with new applicants.

The system pulls notes via the activity endpoint and attaches them as metadata to candidate nodes. This gives your RAG pipeline temporal context about when a candidate was last contacted.

Applicant phone numbers, email addresses, and salary histories pass through an ephemeral V8 sandbox. Vinkius never stores your vector embeddings or raw JSON responses on disk. Authentication happens via a single endpoint token.

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