Which integrations are available for this server?

Saves and retrieves data from MongoDB Atlas, enabling ChatGPT agents to permanently store reports, activity logs, and other structured data.

How do I use AI Agent MCP Server?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@AI Agent MCP Server Save SEO scan result for example.com: status success, score 85" 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.

AI Agent MCP Server

by vishal1145

Overview Schema Source Related Servers Discussions Admin

JavaScript

Remote

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)

Related MCP server: Team Manager MCP

STEP 1 — MongoDB Atlas Setup (Free)

cloud.mongodb.com pe jao
Free account banao
New Project → Create Cluster → M0 Free select karo
Username aur Password set karo (yaad rakhna!)
Network Access → Add IP Address → Allow from anywhere (0.0.0.0/0)

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

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

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)

railway.app pe jao → Free account banao
New Project → Deploy from GitHub repo
Apna ai-agent-mcp repo select karo

Variables tab mein yeh add karo:

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

Deploy click karo

Kuch minutes mein URL milega jaise:

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

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

chatgpt.com → Settings → Developer Mode ON karo
Apna Agent open karo (Edit)
Apps → Custom MCP → Enable

MCP Server URL daalo:

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

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?

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AI Agent MCP Server

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

Yeh Kya Hai?

STEP 1 — MongoDB Atlas Setup (Free)

STEP 2 — GitHub pe Upload Karo

STEP 3 — Railway pe Deploy Karo (Free)

STEP 4 — ChatGPT mein Custom MCP Connect Karo

STEP 5 — Agent Instructions Update Karo

STEP 6 — Data Dekho

Option A: MongoDB Atlas Dashboard

Option B: API se

API Reference

POST /api/save

GET /api/reports/:agentName

GET /api/latest/:agentName

GET /api/agents

Local Testing (Optional)

Project Structure

Problem Aaye Toh?

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

Mongo-MCP

Team Manager MCP

Workspace MCP Server

MongoDB MCP Server