MongoDB That Works - MCP Server

A reliable MongoDB MCP (Model Context Protocol) server that provides seamless MongoDB integration for Claude Desktop with built-in schema discovery and field validation.

Features

• 🔍 Schema Discovery: Automatically analyze collection structures
• ✅ Field Validation: Prevent field name mistakes
• 📊 Full MongoDB Support: Find, aggregate, insert, update, delete operations
• 🚀 High Performance: Efficient connection pooling and query optimization
• 🔐 Secure: Support for MongoDB Atlas and authentication
• 🎯 Type-Safe: Built with TypeScript and Zod validation

Installation

Install from npm

npm install -g @sourabhshegane/mongodb-mcp-that-works

Configuration

Add to your Claude Desktop configuration file:

macOS: `~/Library/Application Support/Claude/claude_desktop_config.json`

Windows: `%APPDATA%\Claude\claude_desktop_config.json`

{
  "mcpServers": {
    "mongodb": {
      "command": "npx",
      "args": ["-y", "@sourabhshegane/mongodb-mcp-that-works@latest"],
      "env": {
        "MONGODB_URI": "mongodb+srv://username:password@cluster.mongodb.net/database",
        "MONGODB_DATABASE": "your_database_name"
      }
    }
  }
}

Configuration Options

• `MONGODB_URI`: Your MongoDB connection string (required)
• `MONGODB_DATABASE`: Default database name (optional)

Available Tools

1. `listCollections`

List all collections in the database.

// Example
mcp.listCollections({ filter: {} })

2. `find`

Find documents in a collection with filtering, sorting, and pagination.

// Example
mcp.find({
  collection: "users",
  filter: { status: "active" },
  sort: { createdAt: -1 },
  limit: 10
})

3. `findOne`

Find a single document.

// Example
mcp.findOne({
  collection: "users",
  filter: { email: "user@example.com" }
})

4. `aggregate`

Run aggregation pipelines.

// Example
mcp.aggregate({
  collection: "orders",
  pipeline: [
    { $match: { status: "completed" } },
    { $group: { _id: "$userId", total: { $sum: "$amount" } } }
  ]
})

5. `count`

Count documents matching a filter.

// Example
mcp.count({
  collection: "products",
  filter: { inStock: true }
})

6. `distinct`

Get distinct values for a field.

// Example
mcp.distinct({
  collection: "orders",
  field: "status"
})

7. `insertOne`

Insert a single document.

// Example
mcp.insertOne({
  collection: "users",
  document: { name: "John Doe", email: "john@example.com" }
})

8. `updateOne`

Update a single document.

// Example
mcp.updateOne({
  collection: "users",
  filter: { _id: "123" },
  update: { $set: { status: "active" } }
})

9. `deleteOne`

Delete a single document.

// Example
mcp.deleteOne({
  collection: "users",
  filter: { _id: "123" }
})

10. `getSchema`

Analyze collection structure and discover field names.

// Example
mcp.getSchema({
  collection: "users",
  sampleSize: 100
})

// Returns:
{
  "collection": "users",
  "sampleSize": 100,
  "fields": {
    "_id": {
      "types": ["ObjectId"],
      "examples": ["507f1f77bcf86cd799439011"],
      "frequency": "100/100",
      "percentage": 100
    },
    "email": {
      "types": ["string"],
      "examples": ["user@example.com"],
      "frequency": "100/100",
      "percentage": 100
    }
  }
}

Best Practices

1. Use Schema Discovery First: Before querying, run `getSchema` to understand field names

2. Handle ObjectIds: The server automatically converts string IDs to ObjectIds

3. Use Projections: Limit returned fields to improve performance

4. Batch Operations: Use aggregation pipelines for complex queries

Examples

Basic Usage

// Get schema first to avoid field name mistakes
const schema = await mcp.getSchema({ collection: "reports" });

// Use correct field names from schema
const reports = await mcp.find({
  collection: "reports",
  filter: { organization_id: "64ba7374f8b63db2083b2665" },
  limit: 10
});

Advanced Aggregation

const analytics = await mcp.aggregate({
  collection: "orders",
  pipeline: [
    { $match: { createdAt: { $gte: new Date("2024-01-01") } } },
    { $group: {
      _id: { $dateToString: { format: "%Y-%m", date: "$createdAt" } },
      revenue: { $sum: "$amount" },
      count: { $sum: 1 }
    }},
    { $sort: { _id: 1 } }
  ]
});

Troubleshooting

Connection Issues

• Verify your MongoDB URI is correct
• Check network connectivity to MongoDB Atlas
• Ensure IP whitelist includes your current IP

Field Name Errors

• Always use `getSchema` to discover correct field names
• Remember MongoDB is case-sensitive
• Check for typos in nested field paths (e.g., "user.profile.name")

Performance

• Use indexes for frequently queried fields
• Limit result sets with `limit` parameter
• Use projections to return only needed fields

License

MIT License - see LICENSE file for details

Changelog

v0.1.0

• Initial release
• Full MongoDB CRUD operations
• Schema discovery tool
• Automatic ObjectId conversion
• TypeScript support

Made out of pain since the official MongoDB MCP didn't work for me