Can my agent list all leave requests for the current year in Factorial?

Yes. Use the 'list_leaves' tool. You can specify the 'year' parameter, and the agent will fetch all absence and holiday requests, allowing you to track team availability flawlessly.

How do I check the shift schedule for a specific month via chat?

Use the 'list_shifts' tool. Provide the 'year' and 'month' (numeric). The agent will retrieve the detailed scheduling information, providing you with a clear view of operational coverage.

Can I audit payslip availability through the agent?

Absolutely. Use the 'list_payslips' tool for a target year and month. Your agent will verify which employees have available compensation records without you needing to manually navigate the payroll section.

Factorial MCP Server for LlamaIndex 12 tools — connect in under 2 minutes

Name: Factorial
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 12 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Factorial as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

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 Factorial. "
            "You have 12 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Factorial?"
    )
    print(response)

asyncio.run(main())

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* 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 Factorial MCP Server

Connect your Factorial HR account to any AI agent and take full control of your human resources management and organizational workflows through natural conversation.

LlamaIndex agents combine Factorial tool responses with indexed documents for comprehensive, grounded answers. Connect 12 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

Employee & Team Orchestration — List all registered employees and teams to retrieve detailed profiles, organizational roles, and department structures natively
Leave & Absence Monitoring — Fetch all holiday and leave requests for any given year to track team availability and upcoming time-off boundaries flawlessly
Shift & Schedule Navigation — Retrieve detailed shift scheduling information for specific months to audit team rotations and operational coverage securely
Payroll Oversight — List available payslips across the organization for specific months to verify compensation records and financial trail metadata
Document Discovery — Access stored company documents and folders to retrieve HR policies and internal documentation using natural language
Company Data Auditing — Fetch global company metadata and administrative configurations to verify workspace settings and organizational identities
Personnel Intelligence — Resolve specific employee contexts including contact details, manager relationships, and hiring dates limitlessly

The Factorial MCP Server exposes 12 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 Factorial to LlamaIndex via MCP

Follow these steps to integrate the Factorial 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 12 tools from Factorial

Why Use LlamaIndex with the Factorial MCP Server

LlamaIndex provides unique advantages when paired with Factorial through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Factorial tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Factorial tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Factorial, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Factorial tools were called, what data was returned, and how it influenced the final answer

Factorial + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the Factorial MCP Server delivers measurable value.

Hybrid search: combine Factorial real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Factorial 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 Factorial for fresh data

Analytical workflows: chain Factorial queries with LlamaIndex's data connectors to build multi-source analytical reports

Factorial MCP Tools for LlamaIndex (12)

These 12 tools become available when you connect Factorial to LlamaIndex via MCP:

clock_in

Clock in for a shift

clock_out

Clock out from a shift

get_employee

Get a specific Factorial employee by ID

get_me

Get current company identity info

list_documents

List all company documents

list_employees

List all Factorial employees

list_folders

List all company folders

list_holidays

List all holidays for a given year

list_leaves

List all leaves for a given year

list_payslips

List all payslips for a given year and month

list_shifts

List all shifts for a given year and month

list_teams

List all Factorial teams

Example Prompts for Factorial in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with Factorial immediately.

"List all employees in the 'Engineering' team"

"Show me upcoming leave requests for June 2026"

"Find HR policy documents in the company folders"

Troubleshooting Factorial MCP Server with LlamaIndex

Common issues when connecting Factorial to LlamaIndex through the Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Factorial + LlamaIndex FAQ

Common questions about integrating Factorial MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Factorial tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.