PlacePilot
README.md
๐ PlacePilot โ AI-Queryable Placement Management Console
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<strong>A live placement management system powered by FastAPI, FastMCP and Model Context Protocol (MCP).</strong>
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<a href="https://project-mcp-server.onrender.com/">
<img src="https://img.shields.io/badge/๐_Live_Project-Open_App-orange?style=for-the-badge" alt="Live Project">
</a>
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๐ Live Demo
๐ Open PlacePilot
MCP Endpoint:
https://project-mcp-server.onrender.com/mcp
PlacePilot is deployed online and provides a web-based placement dashboard together with an MCP endpoint that can be connected to compatible AI tools.
๐ About The Project
PlacePilot is a full-stack placement management console designed to provide a centralized platform for managing and querying student placement information.
The system combines a traditional web dashboard with a Model Context Protocol (MCP) server, allowing compatible AI assistants to interact with live placement data using natural language.
Instead of manually searching through student records, users can ask questions such as:
What is the CGPA of CS101?
What is the attendance percentage of CS101?
Does CS101 have any backlogs?
Is CS101 eligible for TCS?
List the upcoming placement drives.
The MCP server processes these requests through custom tools and retrieves the required information from the placement data.
๐ฏ Problem Statement
Placement coordinators and students often need to work with multiple pieces of information such as:
Student attendance
CGPA
Backlogs
Placement eligibility
Company requirements
Placement drive schedules
When this information is maintained separately, checking eligibility or finding student information can become a manual process.
PlacePilot addresses this by providing a single source of truth for placement information and making that information accessible through both:
A web-based dashboard
AI assistants through MCP
๐ก Solution
PlacePilot provides a centralized placement management system where users can:
Manage student records
Manage placement drives
View attendance and CGPA
Track backlogs
Check placement eligibility
Query placement information using AI
Access the database through a web interface
The MCP layer allows AI-compatible applications to interact with the same underlying placement data.
User
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โ Web Dashboard โ
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โ Placement Data โ
โ / Database โ
โโโโโโโโโโฌโโโโโโโโโ
โฒ
โ
โโโโโโโโโโดโโโโโโโโโ
โ MCP Server โ
โ FastMCP โ
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โผ
โโโโโโโโโโโโโโโโโโโ
โ AI Assistant โ
โ Claude / MCP โ
โ Clients โ
โโโโโโโโโโโโโโโโโโโ
โจ Key Features
๐จโ๐ Student Management
The database dashboard provides functionality to manage student information.
Add student records
View student records
Edit student records
Delete student records
View CGPA
View attendance
Track backlogs
Manage placement-related information
๐ข Placement Drive Management
Placement drives can be managed through the web dashboard.
The system can store information such as:
Company name
Placement drive date
Minimum CGPA
Eligibility requirements
Other placement-related information
๐ค MCP Integration
One of the main features of PlacePilot is its integration with the Model Context Protocol (MCP).
MCP allows AI applications to interact with external tools and data sources in a standardized way.
PlacePilot exposes placement-related functionality through an MCP server.
MCP Endpoint
https://project-mcp-server.onrender.com/mcp
This endpoint can be used by compatible MCP clients.
๐ ๏ธ Custom MCP Tools
PlacePilot provides custom tools for retrieving and processing placement information.
1. get_attendance
Retrieves attendance information for a student.
Example:
What is the attendance percentage of CS101?
2. get_cgpa
Retrieves the CGPA of a student.
Example:
What is the CGPA of CS101?
3. check_eligibility
Checks whether a student meets the eligibility requirements for a placement drive.
Example:
Is CS101 eligible for TCS?
The system checks the available student information and placement requirements before returning the result.
4. list_placement_drives
Retrieves available or upcoming placement drives.
Example:
What placement drives are coming up?
๐ How It Works
A typical AI query follows this flow:
User asks a question
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AI Assistant
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โผ
MCP Protocol
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PlacePilot MCP Server
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โผ
Custom MCP Tool
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Placement Data
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Result
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โผ
AI Assistant
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Natural Language Response
Example
User:
"Is CS101 eligible for TCS?"
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AI identifies the required information
โ
check_eligibility()
โ
Student data + TCS requirements
โ
Eligibility calculation
โ
Result returned to AI
โ
Natural language answer
๐ฅ๏ธ Web Dashboard
PlacePilot also provides a web interface for directly managing the data.
The dashboard includes:
Student statistics
Placement drive statistics
Student records
Placement drive records
Database browser
CRUD operations
Responsive UI
Light/Dark theme
The dashboard and MCP server work with the same placement data.
๐ Dark Mode
The application supports both:
โ๏ธ Light Mode
๐ Dark Mode
The selected theme is persisted so that the interface maintains the user's preferred appearance while navigating between pages.
๐ Dashboard
The dashboard provides an overview of the placement database.
It can display information such as:
Students
Placement Drives
Average CGPA
Recent Students
Upcoming Drives
This provides a quick overview of the current placement data.
๐๏ธ Database Browser
The database browser provides a direct interface for managing placement records.
Users can:
Create
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Read
โ
Update
โ
Delete
student and placement-drive records.
Changes made through the dashboard become available to the other parts of the application using the same underlying data source.
๐งฐ Technology Stack
Backend
Python
FastAPI
FastMCP
Frontend
HTML5
JavaScript
Alpine.js
Tailwind CSS
AI Integration
Model Context Protocol (MCP)
MCP-compatible AI assistants
Deployment
Render
๐๏ธ Project Architecture
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ PlacePilot โ
โ โ
โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ โ
โ โ Web Application โ โ
โ โ โ โ
โ โ Dashboard โ โ
โ โ Database Browser โ โ
โ โ Student Management โ โ
โ โ Placement Drive Management โ โ
โ โโโโโโโโโโโโโโโโโโฌโโโโโโโโโโโโโโโโโโโโ โ
โ โ โ
โ โผ โ
โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ โ
โ โ Backend / API โ โ
โ โ โ โ
โ โ FastAPI โ โ
โ โโโโโโโโโโโโโโโโโโฌโโโโโโโโโโโโโโโโโโโโ โ
โ โ โ
โ โผ โ
โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ โ
โ โ FastMCP โ โ
โ โ โ โ
โ โ get_attendance โ โ
โ โ get_cgpa โ โ
โ โ check_eligibility โ โ
โ โ list_placement_drives โ โ
โ โโโโโโโโโโโโโโโโโโฌโโโโโโโโโโโโโโโโโโโโ โ
โ โ โ
โ โผ โ
โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ โ
โ โ Placement Data โ โ
โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ โ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
๐ Project Structure
PlacePilot/
โ
โโโ static/
โ โโโ style.css
โ โโโ app.js
โ
โโโ templates/
โ โโโ index.html
โ โโโ browser.html
โ โโโ connect.html
โ
โโโ main.py
โโโ requirements.txt
โโโ README.md
โโโ ...
The exact structure may vary depending on the current version of the repository.
๐ Getting Started
Prerequisites
Make sure you have installed:
Python 3.10+
Git
pip
1. Clone the Repository
git clone https://github.com/vishnuprasath2406/YOUR-REPOSITORY-NAME.git
cd YOUR-REPOSITORY-NAME
2. Create a Virtual Environment
Windows
python -m venv venv
venv\Scripts\activate
Linux / macOS
python3 -m venv venv
source venv/bin/activate
3. Install Dependencies
pip install -r requirements.txt
4. Run the Application
Depending on the project's entry point:
uvicorn main:app --reload
The application should then be available at:
http://127.0.0.1:8000
๐ Production Deployment
PlacePilot is deployed using Render.
Live Application
๐ https://project-mcp-server.onrender.com/
MCP Endpoint
๐ https://project-mcp-server.onrender.com/mcp
The live application can be accessed directly through the browser.
๐ Connecting an AI Assistant
PlacePilot's MCP endpoint can be used by compatible MCP clients.
Use the following endpoint:
https://project-mcp-server.onrender.com/mcp
Once connected, the available MCP tools can be used to query placement information.
๐งช Example Queries
After connecting an MCP-compatible AI assistant, you can ask:
Student Information
What is the CGPA of CS101?
What is the attendance of CS102?
Does CS103 have any backlogs?
Eligibility
Is CS101 eligible for TCS?
Which students are eligible for the upcoming placement drive?
Placement Drives
List all upcoming placement drives.
Which companies have a minimum CGPA requirement below 8?
๐ฏ Use Cases
Students
Checking personal placement information
Checking eligibility
Viewing attendance and CGPA requirements
Finding upcoming placement opportunities
Placement Coordinators
Managing student records
Managing placement drives
Checking eligibility
Quickly querying placement information
AI Applications
Retrieving placement information
Calling placement-related tools
Generating natural-language responses
Interacting with structured placement data
๐ Security Note
The current live deployment is configured as a demo application.
The MCP endpoint is publicly accessible for demonstration purposes.
For production deployment, additional security measures should be considered, such as:
Authentication
Authorization
API access control
HTTPS configuration
Rate limiting
Input validation
Secure database credentials
Protected MCP endpoints
๐ฎ Future Enhancements
Potential future improvements include:
๐ User authentication
๐จโ๐ผ Admin and placement-coordinator roles
๐ง Automated placement notifications
๐ Resume management
๐ค AI-powered placement recommendations
๐ Advanced placement analytics
๐ Placement statistics and visualizations
๐ Advanced student search
๐ข Company management
๐ Secure authenticated MCP access
๐ง Additional MCP tools
๐ฑ Improved mobile experience
๐ Project Highlights
Full-Stack Development
Combines a Python backend with a responsive web interface.
MCP Integration
Uses the Model Context Protocol to make placement data accessible to AI-compatible applications.
AI + Database
Instead of creating a separate chatbot with hardcoded responses, the AI interacts with live placement functionality through MCP tools.
Centralized Data
The dashboard and AI tools operate on the same underlying placement data.
Cloud Deployment
The application is deployed online and can be accessed through a public URL.
๐ง What I Learned
Through this project, I gained practical experience in:
Building APIs with FastAPI
Working with FastMCP
Understanding Model Context Protocol
Creating custom MCP tools
Connecting AI applications with external data
Building CRUD-based dashboards
Creating responsive web interfaces
Managing frontend state with Alpine.js
Using Tailwind CSS
Deploying applications using Render
Designing applications around a single source of truth
๐จโ๐ป Author
Vishnu Prasath S
โญ Support
If you find PlacePilot useful or interesting, consider giving the repository a โญ.
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Built with Python, FastAPI, FastMCP, Alpine.js & MCP.
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๐ PlacePilot โ Making placement data accessible through web and AI.
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