---
title: Early Depression Detection MCP Agent
emoji: 🧠
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 6.2.0
app_file: app.py
pinned: true
tags:
- mcp-in-action-track-consumer
- sambanova
- nebius
- huggingface
- depression-detection
- mental-health
- longformer
- mcp
- agents
license: mit
short_description: Agentic depression detection using Kimi K2/Llama 3
---

# 🧠 Early Depression Detection MCP Agent

**Hackathon:** MCP 1st Birthday - Track 2: MCP in Action (Consumer)  
**Author:** Hassan Hassanzadeh Aliabadi | [LinkedIn](https://www.linkedin.com/in/hassanzh/)

## 📹 Demo Video
[YOUTUBE LINK](https://youtu.be/U32a4QjXNz0)

## 🎯 Project Description
This MCP-enabled agent detects depression risk from social media text by orchestrating specialized tools and LLM reasoning. It is built on Master's thesis research and is rigorously validated, achieving an F1-score of **0.7668** on the eRisk 2025 test data.

### 🤖 Multi-Provider Agent Architecture (MCP in Action)

The agent operates in a two-tier structure:

1.  **Tool Layer (The "Eyes"):**
    *   **Tool:** `detect_depression_risk` (My Longformer model).
    *   **Capability:** 4,096-token context window for long user timelines.
    *   **Core Logic:** Implements thesis-derived thresholds (0.4 / 0.6) and classification based on behavioral patterns.

2.  **Reasoning Layer (The "Brain"):**
    *   **Purpose:** Provides empathetic, research-backed interpretation.
    *   **Providers:** Uses **SambaNova** (Meta-Llama-3.3) and **Nebius** (Kimi K2) via the same `OpenAI` client for enhanced robustness and sponsor stacking.

## 🧪 Thesis Findings Integrated
The agent doesn't just output a probability; it looks for and reports on specific linguistic biomarkers identified in my Master's Thesis:
*   **"Nocturnal Posting" & "High-Effort, Low-Frequency":** The primary behavioral signature of high-risk users.
*   **"Echo Chamber Interaction":** The signature of moderate-risk, supportive users engaging with high-risk topics (11.7x higher interaction rate).

## 🏆 Research Background
Built on Master's thesis research at **University of Malaya**:
- **Model:** `avtak/erisk-longformer-depression-v1`.
- **Validation:** Rigorous 5-fold cross-validation.
- **Data Augmentation:** Used Gemini 2.5 Flash Lite to balance the depressed class.

## ⚠️ Ethical Considerations
This is a research tool, **not a medical diagnostic instrument**. 
**Crisis Resources:**
- 🆘 Crisis Text Line: Text HOME to 741741 (US)
- 🌍 International: [befrienders.org](https://befrienders.org)