LiftedWork MCP Server for LlamaIndexGive LlamaIndex instant access to 6 tools to Create Project, Create Task, List Clients, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add LiftedWork 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 App Connector for LlamaIndex
The LiftedWork app connector for LlamaIndex is a standout in the Productivity category — giving your AI agent 6 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 LiftedWork. "
"You have 6 tools available."
),
)
response = await agent.run(
"What tools are available in LiftedWork?"
)
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 LiftedWork MCP Server
Connect your LiftedWork account to any AI agent and manage staffing through natural conversation.
LlamaIndex agents combine LiftedWork tool responses with indexed documents for comprehensive, grounded answers. Connect 6 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 Management — Browse candidates, inspect profiles, and track status
- Placement Tracking — Monitor active placements and contract details
- Job Listings — List open positions and their requirements
The LiftedWork MCP Server exposes 6 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 6 LiftedWork tools available for LlamaIndex
When LlamaIndex connects to LiftedWork through Vinkius, your AI agent gets direct access to every tool listed below — spanning staffing, candidate-management, job-listings, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Create a new project
Pass task data as a JSON string. Create a new task
List all clients
List all projects
List all agency tasks
List all time tracking entries
Connect LiftedWork to LlamaIndex via MCP
Follow these steps to wire LiftedWork into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind the 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 LiftedWork MCP Server
LlamaIndex provides unique advantages when paired with LiftedWork through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine LiftedWork tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain LiftedWork tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query LiftedWork, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what LiftedWork tools were called, what data was returned, and how it influenced the final answer
LiftedWork + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the LiftedWork MCP Server delivers measurable value.
Hybrid search: combine LiftedWork real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query LiftedWork 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 LiftedWork for fresh data
Analytical workflows: chain LiftedWork queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for LiftedWork in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with LiftedWork immediately.
"Show open positions and candidate pipeline status."
"Show candidates for the Senior Developer role."
"Show active placements and contract details."
Troubleshooting LiftedWork MCP Server with LlamaIndex
Common issues when connecting LiftedWork to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpLiftedWork + LlamaIndex FAQ
Common questions about integrating LiftedWork MCP Server with LlamaIndex.