Unanet MCP Server for LlamaIndex 4 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Unanet as an MCP tool provider through the 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 Unanet. "
"You have 4 tools available."
),
)
response = await agent.run(
"What tools are available in Unanet?"
)
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 Unanet MCP Server
The Unanet MCP Server connects AI agents to the Unanet project management and ERP suite. It enables agents to read timesheets, view expense reports, query project statuses, and list organizational workforce data, streamlining compliance and operational efficiency.
LlamaIndex agents combine Unanet tool responses with indexed documents for comprehensive, grounded answers. Connect 4 tools through the 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 Unanet MCP Server exposes 4 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 Unanet to LlamaIndex via MCP
Follow these steps to integrate the Unanet 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 4 tools from Unanet
Why Use LlamaIndex with the Unanet MCP Server
LlamaIndex provides unique advantages when paired with Unanet through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Unanet tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Unanet tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Unanet, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Unanet tools were called, what data was returned, and how it influenced the final answer
Unanet + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Unanet MCP Server delivers measurable value.
Hybrid search: combine Unanet real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Unanet 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 Unanet for fresh data
Analytical workflows: chain Unanet queries with LlamaIndex's data connectors to build multi-source analytical reports
Unanet MCP Tools for LlamaIndex (4)
These 4 tools become available when you connect Unanet to LlamaIndex via MCP:
expenses
List expense reports for a user
projects
List projects in Unanet
timesheets
List timesheets for a user
users
List users/employees in Unanet
Example Prompts for Unanet in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Unanet immediately.
"Retrieve the submitted expense reports for Project X."
"List all pending timesheets for the engineering department this week."
"Check the current compliance status and budget utilization for the 'Alpha-1' defense contract."
Troubleshooting Unanet MCP Server with LlamaIndex
Common issues when connecting Unanet to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpUnanet + LlamaIndex FAQ
Common questions about integrating Unanet 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 Unanet 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 Unanet to LlamaIndex
Get your token, paste the configuration, and start using 4 tools in under 2 minutes. No API key management needed.