# Employee Salary Benchmark MCP for AI Agents MCP

> Employee Salary Benchmark gives your AI client precise salary bounds in USD and BRL. It pulls current market data for specific roles, seniority levels, and startup stages worldwide, letting you benchmark compensation instantly. Stop guessing pay ranges; use this MCP to analyze global pay trends across major tech hubs like London or San Francisco.

## Overview
- **Category:** finance
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_Qzq5IS8Dov8GlCk8C6XU1s7xC0R9zgoyRtLVK7Va/mcp
- **Tags:** salary, benchmark, compensation, startup, recruitment

## Description

This connector lets your AI agent access a specialized dataset of real-world market compensation data. Instead of wading through multiple industry reports and outdated salary guides, you simply ask for the numbers—minimums and maximums in both USD and BRL. You can analyze how pay changes based on specific professional roles or if an employee moves from junior to senior level. Need to compare salaries between different geographic hubs, say London versus San Francisco? This MCP handles that complexity. Whether you're dealing with pre-seed funding rounds up through Series B startups, you get precise salary bounds for the whole spectrum. Because Vinkius hosts this MCP in its catalog, your AI client connects once and gains immediate access to advanced compensation analysis tools.

## Tools

### calculate_average_salary
Calculates the average minimum salary across a list of specified professional roles.

### get_salary_range
Retrieves the minimum and maximum expected salary bounds for any given professional profile.

### compare_seniority_premium
Estimates the percentage increase in compensation when an employee moves up seniority levels.

## Prompt Examples

**Prompt:** 
```
What is the salary range for a senior data scientist in San Francisco at a Series B startup?
```

**Response:** 
```
**Salary Benchmark: Data Scientist**

*   **Location:** San Francisco, CA
*   **Stage:** Series B
*   **Role:** Senior Data Scientist

| Metric | Min Salary (USD) | Max Salary (USD) |
| :--- | :---: | :---: |
| **Annual Range** | $160,000 | $210,000 |
*Note: This range represents the estimated base salary and does not include equity or bonus.*
```

**Prompt:** 
```
Can I compare salaries for engineering vs. product management in London?
```

**Response:** 
```
**Compensation Comparison (London)**

| Role | Average Salary Est. (USD) |
| :--- | :---: |
| Software Engineer | $90,000 - $125,000 |
| Product Manager | $85,000 - $110,000 |

*These averages are calculated based on current market data across various startup sizes.*
```

**Prompt:** 
```
How much more should I expect if I move from junior to mid-level marketing?
```

**Response:** 
```
**Seniority Premium Calculation (Marketing)**

Moving from a Junior Marketing role to a Mid-Level Marketing role in the current market typically results in an estimated **35% increase** in the middle of the salary range. This premium accounts for increased autonomy and project ownership.
```

## Capabilities

### Retrieve salary bounds for a role
Gets the minimum and maximum salary range for a specific professional profile.

### Calculate average compensation across roles
Determines the average starting salary across a list of different job roles.

### Compare pay jumps between levels
Calculates the estimated percentage increase in pay when an employee moves up seniority levels.

## Use Cases

### Checking if a new offer is competitive
A recruiter needs to know the pay ceiling for a mid-level designer in London at a seed stage startup. They ask their agent, which uses `get_salary_range` and gets an immediate answer: $60k - $85k USD.

### Justifying salary adjustments
The HR team needs to prove the pay difference between two roles. They use `calculate_average_salary` to compare average salaries for engineers versus product managers in Berlin, providing immediate data points for negotiation.

### Planning promotions and raises
A manager wants to promote an employee from junior to senior level. They check the expected raise using `compare_seniority_premium`, which shows a reliable 45% increase, allowing them to set accurate compensation expectations.

### Analyzing regional pay discrepancies
The executive team needs an overview of cost differences. They use the MCP to compare average salaries for similar roles across multiple global hubs like San Francisco and Singapore in one query.

## Benefits

- Pinpoint exact pay ranges. Instead of using rough estimates, you get specific minimum and maximum salary bounds in USD and BRL from the `get_salary_range` tool.
- Understand career growth value. Use the `compare_seniority_premium` function to quantify exactly how much more money an engineer should expect when moving from a junior to a senior role.
- Benchmark departmental pay structures. Run analyses using `calculate_average_salary` to compare average compensation across different job families, like engineering versus design.
- Handle multi-market complexity. Compare salary data for multiple geographic hubs (e.g., London vs. San Francisco) in one go, eliminating tedious spreadsheet work.
- Target specific funding stages. Filter results by startup stage, from pre-seed through Series B, ensuring your compensation advice is highly relevant to the company's current funding status.

## How It Works

The bottom line is that you get immediate, benchmarked pay estimates without manual research.

1. Your AI client sends a request, specifying the job role, location (e.g., London), and startup stage (e.g., Series A).
2. The MCP processes this data against its comprehensive market compensation dataset.
3. Your agent receives structured salary bounds or average increases in both USD and BRL.

## Frequently Asked Questions

**How does the Employee Salary Benchmark MCP handle different global currencies?**
The MCP provides precise compensation data in both USD and BRL, so you don't have to worry about manual currency conversions. It gives you direct bounds for multiple markets simultaneously.

**Can I use the Employee Salary Benchmark MCP to check salaries across different startup stages?**
Yes. You can specify funding stages, from pre-seed all the way through Series B. This ensures the salary range you get is relevant to the company's current size and market maturity.

**Is the data in Employee Salary Benchmark accurate for my specific industry?**
The MCP uses a specialized dataset covering various professional roles across multiple tech sectors. While it provides strong benchmarks, always cross-reference with local HR counsel for final policy decisions.

**What if I need to compare salaries between two different cities? Does the Employee Salary Benchmark MCP support that?**
Absolutely. You can query compensation data for multiple geographic hubs (like comparing London to San Francisco) and get a comparative view of market rates in one go.

**Does the salary data I get from Employee Salary Benchmark include equity or just base pay?**
The provided ranges are designed to give you accurate base salary bounds. While it doesn't calculate total compensation including complex stock options, it gives you a solid starting point for your negotiation.