Intern Task Tracker - MCP Server

A Python-based Model Context Protocol (MCP) Server developed for tracking daily internship work.

The project uses FastMCP to expose task-management tools and SQLite to permanently store Daily Work Log information.

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Project Overview

The Intern Task Tracker allows internship work details to be managed through MCP tools.

The current system supports:

• Adding Daily Work Logs
• Listing Daily Work Logs
• Updating a work log status to Done
• Deleting Daily Work Logs
• Viewing an overall work summary
• Storing all work log information in SQLite
• Testing MCP tools through MCP Inspector

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Technologies Used

• Python
• Model Context Protocol (MCP)
• FastMCP
• SQLite
• MCP Inspector
• VS Code
• Node.js / NPX

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Project Structure

MCP_Server/
│
├── server.py
├── database.py
├── intern_tracker.db
├── requirements.txt
├── README.md
│
├── venv/
│
└── __pycache__/

server.py

Contains the MCP server and all MCP tools.

database.py

Handles:

• SQLite connection
• Database initialization
• Table creation
• Database reset functionality

intern_tracker.db

SQLite database used to permanently store Daily Work Log records.

requirements.txt

Contains the Python dependencies required by the project.

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Daily Work Log Structure

The daily_work_log table stores the following information:

Field	Description
id	Unique ID of the work log
work_date	Date of work
day	Day automatically calculated from date
task_description	Work performed / task description
deliverables	Deliverables or output
blockers	Blockers or dependencies
hours_spent	Total working hours
status	Current task status
notes	Additional comments
created_at	Record creation timestamp
updated_at	Last update timestamp

When a new Daily Work Log is created, its status is automatically:

To Do

The update_work_status MCP tool changes the status to:

Done

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MCP Tools

The server currently provides five MCP tools.

1. add_daily_work_log

Adds a new Daily Work Log to the SQLite database.

Inputs include:

• Work Date
• Task Description
• Deliverables
• Blockers
• Hours Spent
• Notes

The day is automatically calculated from the entered date.

The default status is automatically set to To Do.

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2. list_daily_work_logs

Retrieves the Daily Work Logs stored in the database.

Logs can optionally be filtered using their status.

Examples:

To Do
Done

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3. update_work_status

Marks an existing Daily Work Log as:

Done

The Log ID is required to identify the record.

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4. delete_daily_work_log

Deletes a Daily Work Log using its Log ID.

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5. get_work_summary

Returns a summary containing:

• Total Work Logs
• Completed Logs
• To Do Logs
• Total Hours Spent

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Setup Instructions

1. Open the Project

Open the project folder in VS Code:

cd D:\MCP_Server

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2. Activate Virtual Environment

Run:

.\venv\Scripts\Activate.ps1

The terminal should show:

(venv) PS D:\MCP_Server>

PowerShell Execution Policy Issue

If PowerShell prevents the virtual environment from activating, run:

Set-ExecutionPolicy -Scope Process -ExecutionPolicy RemoteSigned

Then:

.\venv\Scripts\Activate.ps1

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Running the Project

The following commands are used to verify and run the MCP server.

Step 1 - Check server.py for syntax errors

Run:

python -m py_compile server.py

If no error appears, the Python file compiled successfully.

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Step 2 - Verify the MCP Server

Run:

python -c "from server import mcp; print('Server loaded successfully')"

Expected output:

Server loaded successfully

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Step 3 - Initialize the SQLite Database

Run:

python database.py

Expected output:

==================================================
Intern Tracker Database Initialized Successfully
==================================================

This creates the database/table if it does not already exist.

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Step 4 - Start MCP Inspector

Run:

npx @modelcontextprotocol/inspector python server.py

Expected output will be similar to:

Starting MCP inspector...

MCP Inspector Web is up and running at:
http://localhost:6274?MCP_INSPECTOR_API_TOKEN=...

Sandbox (MCP Apps):
http://localhost:xxxxx/sandbox

Auth token: ...

Opening browser...

The port numbers and authentication token can change every time MCP Inspector starts.

The browser should open MCP Inspector automatically.

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Quick Run Commands

For normal development, use:

cd D:\MCP_Server

.\venv\Scripts\Activate.ps1

python -m py_compile server.py

python database.py

npx @modelcontextprotocol/inspector python server.py

For additional server verification, run:

python -c "from server import mcp; print('Server loaded successfully')"

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Using MCP Inspector

After MCP Inspector opens:

1. Open the Tools section.
2. Select add_daily_work_log.
3. Enter the work log information.
4. Click Execute Tool.
5. Select list_daily_work_logs.
6. Execute the tool to verify that the record was stored.
7. Use update_work_status to mark a work log as Done.
8. Use delete_daily_work_log when a record needs to be removed.
9. Use get_work_summary to view the overall internship work summary.

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Data Flow

    MCP Inspector
          |
          v
      MCP Tool
          |
          v
      server.py
          |
          v
     database.py
          |
          v
        SQLite
          |
          v
    intern_tracker.db
          |
          v
    daily_work_log

For example:

User enters Daily Work Log
          |
          v
   add_daily_work_log
          |
          v
    INSERT SQL Query
          |
          v
   intern_tracker.db
          |
          v
  daily_work_log table

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Viewing Stored Data

The intern_tracker.db file is a binary SQLite database file, so it should not be opened as a normal text file.

Use a SQLite viewer/editor extension in VS Code.

Open:

intern_tracker.db

Then select:

TABLES
   └── daily_work_log

The stored records will be displayed in table format.

You can also verify the data from the terminal:

python -c "import sqlite3; con=sqlite3.connect('intern_tracker.db'); rows=con.execute('SELECT * FROM daily_work_log').fetchall(); print(rows); con.close()"

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Current Project Status

The following functionality has been completed:

• [x] Python MCP server setup
• [x] FastMCP integration
• [x] SQLite database setup
• [x] Daily Work Log table
• [x] Add Daily Work Log
• [x] List Daily Work Logs
• [x] Update Work Status
• [x] Delete Daily Work Log
• [x] Work Summary
• [x] Automatic day calculation
• [x] Default To Do status
• [x] MCP Inspector integration
• [x] SQLite data persistence
• [x] End-to-end MCP tool testing

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Future Development

Future versions of the project can include:

• MCP Resources
• MCP Prompts
• Additional validation
• Duplicate work-date handling
• Improved reporting and summaries

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Developer

Soham Thoke

AI Engineering Intern

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Project Purpose

This project was developed to understand and implement a Python-based MCP server while building a practical internship Daily Work Log tracking system using MCP tools and SQLite.