Can I automatically fetch raw PDF files of individual payslips?

Currently the AI fetches structured JSON metadata representing the explicit payslip limits generated in the pay run, rather than arbitrary PDF media binaries. This ensures high-speed, secure token-based programmatic observation.

Do I need special scopes permitted via PayFit's platform security?

Yes. Ensure the API token generated internally on PayFit's Admin panel is bound physically to 'Collaborators', 'Payslips', and 'Accounting' read boundaries. Without the proper scope, the endpoints strictly deny payloads.

How do accounting entries handle multiple logical company subsidiaries?

The tool bounds its exact logic based directly on the explicit `Company ID` provided in the plugin settings. To query disparate subsidiaries, adjust the Company ID dynamically.

PayFit MCP Server for LlamaIndex 7 tools — connect in under 2 minutes

Name: PayFit
Availability: InStock
Author: Vinkius

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

Built by Vinkius GDPR 7 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add PayFit 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 PayFit. "
            "You have 7 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in PayFit?"
    )
    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 PayFit MCP Server

Bring PayFit Global Payroll into your automated AI workflows natively. Providing a strict programmatic bridge to your company's HR and accounting infrastructure, this agent dynamically maps active employees, monitors valid compliance contracts, securely extracts monthly payslip distributions, and generates valid accounting entries directly via chat.

LlamaIndex agents combine PayFit tool responses with indexed documents for comprehensive, grounded answers. Connect 7 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

Company Overview — Identify exact corporate metadata determining mapping addresses, strict payroll limits, and department structures implicitly
Collaborator Navigation — Locate specific team boundaries, retrieving exactly which individual holds which contract running securely across active modules
Payslip Operations — Identify discrete payslip runs fetching securely masked data tracking payroll compliance outputs
Ledger Generation — Execute direct extractions generating valid mapping endpoints matching automated accounting metrics and financial records

The PayFit MCP Server exposes 7 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 PayFit to LlamaIndex via MCP

Follow these steps to integrate the PayFit 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 7 tools from PayFit

Why Use LlamaIndex with the PayFit MCP Server

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

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

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

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

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

PayFit + LlamaIndex Use Cases

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

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

Data enrichment: query PayFit 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 PayFit for fresh data

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

PayFit MCP Tools for LlamaIndex (7)

These 7 tools become available when you connect PayFit to LlamaIndex via MCP:

get_accounting_entries

Get accounting entries for a specific payroll period (YYYYMM format)

get_collaborator_details

Get detailed information about a specific collaborator

get_company

Get overview information about the PayFit company account

list_collaborators

List all collaborators (employees) in the company

list_contracts

List all employment contracts in the company

list_departments

List all departments in the company

list_payslips

List all payslips for a specific collaborator

Example Prompts for PayFit in LlamaIndex

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

"Fetch the metadata configuration belonging to our main running PayFit company entity."

"Scan our active architecture fetching a strict list of all collaborators and contracts."

"Retrieve global accounting transactions executed during March."

Troubleshooting PayFit MCP Server with LlamaIndex

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

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

PayFit + LlamaIndex FAQ

Common questions about integrating PayFit 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 PayFit tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.