A
license-
qualityB
maintenanceAn intelligent, zero-cost MCP server that securely parses local resumes and compares them against live job postings for ATS scoring, skill gap analysis, and interview prep.
1
MIT
Placement Analyzer MCP
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Placement Analyzer MCPAnalyze skill gaps for final year CSE students from the imported data."
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.
Placement Analyzer MCP
Production-grade Model Context Protocol (MCP) server for analyzing student placement profiles.
Designed for college placement cells that receive student data in bulk and need structured, evidence-backed intelligence.
Architecture
MCP Tool
↓
Application Service
↓
Domain / Business Logic
↓
Repository
↓
PostgreSQLData Flow
Excel (.xlsx)
↓
Validation → Column Mapping → Normalization → Duplicate Detection
↓
PostgreSQL
↓
Intelligence Engine (Resume, GitHub, LeetCode, Portfolio)
↓
Evidence System → Skill Gaps → Profile Completeness
↓
↓
MCP Tools & Resources
↓
College-wide Placement Analytics (Supply/Demand, Competitiveness)
## Quick Start
### Prerequisites
- Python 3.12+
- Docker & Docker Compose (for local PostgreSQL)
### Setup
```bash
# Clone the repository
git clone https://github.com/mr-sanjai-offl/talentlens-mcp.git
cd talentlens-mcp
# Create environment file
cp .env.example .env
# Start PostgreSQL
docker compose up postgres -d
# Install dependencies (using uv)
uv sync --all-extras
# Or using pip
pip install -e ".[dev]"
# Run database migrations
alembic upgrade head
# Start the MCP server
python -m placement_analyzer.mcp.serverDocker Compose (Full Stack)
docker compose up --buildRelated MCP server: Resume MCP Server
MCP Tools
Group | Tool | Status |
Ingestion |
| Phase 2 |
| Phase 2 | |
| Phase 2 | |
Students |
| ✅ Active |
| ✅ Active | |
| ✅ Active | |
Analysis |
| Phase 3 |
| Phase 3 | |
| Phase 3 | |
| Phase 3 | |
| Phase 3 | |
Intelligence |
| Phase 4 |
| Phase 4 | |
| Phase 4 | |
Analytics |
| Phase 4 |
| Phase 4 | |
| Phase 4 | |
Decision |
| Phase 7 |
| Phase 7 | |
| Phase 7 | |
| Phase 7 | |
| Phase 7 | |
Copilot |
| Phase 7 |
| Phase 7 | |
| Phase 7 | |
Analytics (V2) |
| Phase 8 |
| Phase 8 | |
| Phase 8 | |
| Phase 8 | |
| Phase 8 | |
| Phase 8 |
MCP Resources
URI | Description |
| Full student profile |
| Completeness & status |
| Skills with evidence |
| Full evidence tree |
Project Structure
src/placement_analyzer/
├── core/ # Errors, enums, types, logging
├── config/ # Pydantic settings
├── database/ # Models, repositories, engine
├── schemas/ # Pydantic request/response models
├── ingestion/ # Excel parsing, validation, normalization
├── intelligence/ # Resume, GitHub, LeetCode, Portfolio analysis
├── services/ # Business logic orchestration
└── mcp/ # MCP server, tools, resourcesDevelopment
# Run tests
pytest -v
# Lint
ruff check src/ tests/
# Format
ruff format src/ tests/
# Type check
mypy src/Environment Variables
Variable | Default | Description |
|
| Async database URL |
|
| Connection pool size |
|
| Logging level |
|
|
|
| — | GitHub API token (optional) |
|
| Max upload file size |
Technology Stack
Python 3.12+ with strict typing
MCP SDK v2 (
MCPServer)SQLAlchemy 2.x (async) + asyncpg
Alembic for migrations
Pydantic 2.x for validation
structlog for structured logging
Docker for deployment
License
MIT
Maintenance
–Maintainers
–Response time
–Release cycle
–Releases (12mo)
Commit activity
Resources
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Looking for Admin?
If you are the server author, to access and configure the admin panel.
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