How do I use Leave Management MCP Server?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Leave Management MCP Server Show leave balance for E001" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Leave Management MCP Server

by ratankumarthakur

Overview Schema Source Related Servers Discussions Admin

Python

Local

Leave Management MCP Server

An AI-powered Leave Management System built using the Model Context Protocol (MCP).

This project demonstrates how a Large Language Model (LLM) can discover and invoke MCP tools to perform business operations such as checking leave balances, applying for leave, and retrieving leave history through natural language.

Features

Built using the FastMCP framework
Streamlit-based AI client
Gemini API for intelligent tool selection
SQLite database for persistent data storage
Dynamic MCP tool discovery using list_tools()
Natural language interface

Related MCP server: leave-manager-mcp

Project Structure

.
├── app.py              # Streamlit AI Client
├── main.py             # MCP Server
├── llm.py              # Gemini Integration
├── database.py         # SQLite Helper Functions
├── init_db.py          # Database Initialization
├── pyproject.toml
├── uv.lock
├── .python-version
├── .env.example
├── README.md
└── .gitignore

Available MCP Tools

get_leave_balance
apply_leave
get_leave_history

Tech Stack

Python
Model Context Protocol (MCP)
FastMCP
Streamlit
Gemini API
SQLite
uv

Installation

1. Clone the repository

git clone https://github.com/ratankumarthakur/leave-management-mcp
cd leave-management-mcp

2. Install dependencies

uv sync

3. Configure the environment

Create a .env file in the project root.

GEMINI_API_KEY=YOUR_API_KEY

4. Initialize the database

python init_db.py

5. Run the application

streamlit run app.py

Example Queries

Try asking:

Show leave balance for E001
Apply leave for E002 on 2026-08-10
Show leave history for E001
Apply leave for E001 on 15 September 2026

Architecture

User
   │
   ▼
Streamlit Client
   │
   ▼
Gemini LLM
   │
   ▼
MCP Client
   │
   ▼
MCP Server
   │
   ▼
SQLite Database

How It Works

The user enters a natural language query.
The Gemini model selects the appropriate MCP tool.
The Streamlit client invokes the selected MCP tool.
The MCP server executes the requested operation.
Data is read from or written to the SQLite database.
The result is returned to the client and displayed to the user.

Learning Objective

This project was built to understand the fundamentals of the Model Context Protocol (MCP), including:

Building an MCP server
Creating an MCP client
Dynamic tool discovery
LLM-driven tool invocation
Database-backed tool execution
Developing an AI-powered application using Streamlit

Screenshot

Available Tools

3 tools

apply_leaveD

ParametersJSON Schema

Name	Required	Description	Default
`employee_id`	Yes
`leave_dates`	Yes

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

D1/5.0

Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Tool has no description.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness1/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Tool has no description.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Tool has no description.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Tool has no description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose1/5

Does the description clearly state what the tool does and how it differs from similar tools?

Tool has no description.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines1/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Tool has no description.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_leave_balanceD

ParametersJSON Schema

Name	Required	Description	Default
`employee_id`	Yes

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

D1/5.0

Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Tool has no description.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness1/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Tool has no description.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Tool has no description.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Tool has no description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose1/5

Does the description clearly state what the tool does and how it differs from similar tools?

Tool has no description.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines1/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Tool has no description.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_leave_historyD

ParametersJSON Schema

Name	Required	Description	Default
`employee_id`	Yes

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

D1/5.0

Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Tool has no description.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness1/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Tool has no description.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Tool has no description.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Tool has no description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose1/5

Does the description clearly state what the tool does and how it differs from similar tools?

Tool has no description.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines1/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Tool has no description.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

3 tool updatesv0.1.0Aug 4, 2026
- First observedapply_leave
- First observedget_leave_balance
- First observedget_leave_history

Deploy Server

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

C2.1/5.0

Scored 2026-08-04 18:40 across 3 tools

Disambiguation5/5

Each tool covers a distinct aspect: balance inquiry, leave application, and history lookup. There is no overlap or ambiguity between them.

Naming Consistency5/5

All tool names follow the consistent verb_noun pattern: get_leave_balance, apply_leave, get_leave_history. The naming is clear and predictable.

Tool Count5/5

Three tools are well-scoped for a simple leave management server, covering the core employee-facing actions without unnecessary bloat.

Completeness4/5

Core leave lifecycle is covered (check balance, apply, view history), but missing operations like cancel or update leave applications are notable gaps.

Maintenance

ActivitySlowing

ResponsivenessNo issues

Resources

GitHub Repository

Need Help?

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