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AI Agent MCP Server

by vishal1145
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AI Agent MCP Server

ChatGPT Agent Reports ko MongoDB mein store karo — Step by Step Guide

Yeh Kya Hai?

ChatGPT ke scheduled agents kaam karte hain aur reports apni chat mein store karte hain. Yeh server ek bridge hai jo:

  • ChatGPT Agent se data receive karta hai (Custom MCP ya REST API)

  • MongoDB Atlas mein permanently store karta hai

  • Kisi bhi time data retrieve karne deta hai

⏰ ChatGPT Scheduled Agent
        ↓
🔧 Yeh MCP Server (/mcp endpoint)
        ↓
💾 MongoDB Atlas Database
        ↓
📊 Kabhi bhi data dekho (API ya Atlas Dashboard)

STEP 1 — MongoDB Atlas Setup (Free)

  1. cloud.mongodb.com pe jao

  2. Free account banao

  3. New Project → Create Cluster → M0 Free select karo

  4. Username aur Password set karo (yaad rakhna!)

  5. Network Access → Add IP Address → Allow from anywhere (0.0.0.0/0)

  6. Connect → Drivers → Node.js → Connection string copy karo:

    mongodb+srv://USERNAME:PASSWORD@cluster0.xxxxx.mongodb.net/ai_agents

  7. Yeh string save kar lo — baad mein chahiye hogi

STEP 2 — GitHub pe Upload Karo

# Project folder mein jao
cd ai-agent-mcp

# Git initialize karo
git init
git add .
git commit -m "Initial commit"

# GitHub pe new repository banao: github.com/new
# Phir yeh commands chalao:
git remote add origin https://github.com/TERA_USERNAME/ai-agent-mcp.git
git push -u origin main

STEP 3 — Railway pe Deploy Karo (Free)

  1. railway.app pe jao → Free account banao

  2. New Project → Deploy from GitHub repo

  3. Apna ai-agent-mcp repo select karo

  4. Variables tab mein yeh add karo:

    MONGO_URI = mongodb+srv://USERNAME:PASSWORD@cluster0.xxxxx.mongodb.net/ai_agents
PORT = 3000

  5. Deploy click karo

  6. Kuch minutes mein URL milega jaise:

    https://ai-agent-mcp-production.up.railway.app

  7. Browser mein kholo → {"status": "✅ AI Agent MCP Server is running!"} dikhega

Yeh URL save kar lo — ChatGPT mein daalna hai!

STEP 4 — ChatGPT mein Custom MCP Connect Karo

  1. chatgpt.com → Settings → Developer Mode ON karo

  2. Apna Agent open karo (Edit)

  3. Apps → Custom MCP → Enable

  4. MCP Server URL daalo:

    https://ai-agent-mcp-production.up.railway.app/mcp

  5. Save karo → Tools appear honge:

    • save_data

    • get_data

    • get_latest

    • log_activity

STEP 5 — Agent Instructions Update Karo

Agent ke Instructions mein yeh add karo:

IMPORTANT: Har task complete karne ke baad HAMESHA yeh karo:

1. Apna kaam karo (SEO check / analysis / report)
2. save_data tool call karo:
   - agentName: "[TERA AGENT KA NAAM]"
   - taskType: "[kya kiya, e.g. seo_scan]"
   - status: "success" ya "failed"
   - payload: {
       summary: "kya mila",
       details: [...findings...],
       recommendations: [...suggestions...]
     }
   - metadata: {
       url: "[website jo check ki]",
       model: "gpt-4",
       duration: "[kitna time laga]"
     }

3. Kabhi bhi sirf chat mein result mat rakho
4. Hamesha database mein save karo

STEP 6 — Data Dekho

Option A: MongoDB Atlas Dashboard

  • cloud.mongodb.com → Apna cluster → Browse Collections

  • ai_agents database → agentdatas collection

Option B: API se

# Sab agents dekho
GET https://tera-server.up.railway.app/api/agents

# Specific agent ki reports
GET https://tera-server.up.railway.app/api/reports/SEO%20Agent

# Latest report
GET https://tera-server.up.railway.app/api/latest/SEO%20Agent

# Filter karo
GET https://tera-server.up.railway.app/api/reports/SEO%20Agent?taskType=seo_scan&limit=5

API Reference

POST /api/save

{
  "agentName": "SEO Agent",
  "taskType": "seo_scan",
  "status": "success",
  "payload": {
    "website": "example.com",
    "score": 85,
    "issues": ["Missing meta description", "Slow page speed"],
    "recommendations": ["Add meta tags", "Optimize images"]
  },
  "metadata": {
    "url": "https://example.com",
    "checkedAt": "2024-01-15T09:00:00Z"
  }
}

GET /api/reports/:agentName

Query params: limit, page, taskType, status

GET /api/latest/:agentName

GET /api/agents

Local Testing (Optional)

# Dependencies install karo
npm install

# .env file banao
cp .env.example .env
# .env mein MONGO_URI daalo

# Server start karo
npm run dev

# Test karo
curl -X POST http://localhost:3000/api/save \
  -H "Content-Type: application/json" \
  -d '{"agentName":"Test Agent","taskType":"test","payload":{"message":"Hello!"}}'

Project Structure

ai-agent-mcp/
├── server.js          ← Main entry point
├── package.json       ← Dependencies
├── railway.toml       ← Railway deploy config
├── .env.example       ← Environment variables template
├── .gitignore
├── models/
│   └── AgentData.js   ← MongoDB schema
├── routes/
│   └── api.js         ← REST API endpoints
└── mcp/
    └── tools.js       ← MCP tools (save_data, get_data, etc.)

Problem Aaye Toh?

Problem

Solution

MongoDB connect nahi

IP whitelist check karo (0.0.0.0/0 hona chahiye)

Railway deploy fail

Logs check karo → Variables mein MONGO_URI sahi daala?

ChatGPT MCP nahi dikha

Developer Mode ON hai? Business/Plus plan chahiye

Tools appear nahi

MCP URL mein /mcp path daala?

This server cannot be installed

F
license - not found
-
quality - not tested
C
maintenance

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