Flatwork ATS MCP Server for LlamaIndexGive LlamaIndex instant access to 8 tools to Create Applicant, Get Applicant, Get Job, and more
GDPR Free for SubscribersLlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Flatwork 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 for LlamaIndex
The Flatwork ATS MCP Server for LlamaIndex is a standout in the Productivity category — giving your AI agent 8 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
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 Flatwork ATS. "
"You have 8 tools available."
),
)
response = await agent.run(
"What tools are available in Flatwork 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 Flatwork ATS MCP Server
Connect your Flatwork ATS account to any AI agent and take full control of your recruitment pipeline and candidate management workflows through natural conversation.
LlamaIndex agents combine Flatwork 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
- Job Orchestration — List all open and closed job postings and retrieve detailed metadata, including hiring teams and job requirements programmatically
- Candidate Tracking — Manage your complete directory of applicants and retrieve detailed profiles and contact information programmatically
- Application Lifecycle — Monitor active job applications and update candidate hiring stages (Interview, Hired, Rejected) directly through your agent
- Applicant Discovery — Programmatically create new candidates in the system using external data to automate your sourcing pipeline
- System Monitoring — List configured webhooks to understand real-time data flows and ensure high-fidelity synchronization with your HR tools
The Flatwork 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.
All 8 Flatwork ATS tools available for LlamaIndex
When LlamaIndex connects to Flatwork ATS through Vinkius, your AI agent gets direct access to every tool listed below — spanning hiring-pipeline, candidate-tracking, job-postings, and more. Every call runs in a secure, isolated environment with full audit visibility. Beyond a simple connection, you get real-time monitoring of agent activity, enterprise governance, and optimized token usage.
Pass applicant data as a JSON string. Add a new candidate
Get applicant details
Get job details
List all applicants/candidates
List all job applications
List all job postings
List configured webhooks
Update application hiring stage
Connect Flatwork ATS to LlamaIndex via MCP
Follow these steps to wire Flatwork ATS into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the Flatwork ATS MCP Server
LlamaIndex provides unique advantages when paired with Flatwork ATS through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Flatwork ATS tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Flatwork ATS tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Flatwork ATS, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Flatwork ATS tools were called, what data was returned, and how it influenced the final answer
Flatwork ATS + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Flatwork ATS MCP Server delivers measurable value.
Hybrid search: combine Flatwork ATS real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Flatwork 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 Flatwork ATS for fresh data
Analytical workflows: chain Flatwork ATS queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for Flatwork ATS in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Flatwork ATS immediately.
"List all active job postings in Flatwork ATS."
"Add 'John Doe' (john.doe@example.com) as a new applicant."
"Move application ID 'app_987' to the 'Interview' stage."
Troubleshooting Flatwork ATS MCP Server with LlamaIndex
Common issues when connecting Flatwork ATS to LlamaIndex through Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpFlatwork ATS + LlamaIndex FAQ
Common questions about integrating Flatwork ATS MCP Server with LlamaIndex.