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MCP MongoDB Server

MongoDB (Official)

75·Strong

Overall Trust Score

Community-maintained MCP server for MongoDB database operations. Enables AI models to query, insert, update, and delete documents, manage collections, create indexes, and perform aggregations. Essential for AI-powered NoSQL database management and data analysis workflows.

database
nosql
mcp
model-context-protocol
Version: 1.0.0
Last Evaluated: November 9, 2025
Official Website →

Trust Vector

Performance & Reliability

83
query reliability
88
Methodology
Query success rate testing
Evidence
MongoDB Driver
Built on official MongoDB drivers with stable query execution
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
aggregation accuracy
85
Methodology
Aggregation testing
Evidence
MongoDB Aggregation
Accurate aggregation pipeline execution
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
large dataset handling
78
Methodology
Scalability testing
Evidence
Implementation Review
Performance degrades with very large result sets; pagination needed
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
connection stability
82
Methodology
Connection stability testing
Evidence
MongoDB Connection Pooling
Stable connections with pooling and automatic reconnection
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
error recovery
82
Methodology
Error handling testing
Evidence
Implementation Review
Handles database errors with retry logic and error reporting
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09

Security

68
authentication security
78
Methodology
Authentication mechanism review
Evidence
MongoDB Authentication
Supports MongoDB authentication mechanisms (SCRAM, X.509)
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
connection string exposure
60
Methodology
Credential security analysis
Evidence
MCP Security Model
Connection string with credentials stored locally; accessible to AI
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
query injection risk
65
Methodology
Injection vulnerability testing
Evidence
Security Analysis
AI can construct arbitrary queries; NoSQL injection risk if not validated
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
data modification control
62
Methodology
Operation authorization testing
Evidence
MongoDB Permissions
AI can insert, update, and delete documents within user permissions
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
database drop risk
70
Methodology
Destructive operation testing
Evidence
Implementation Review
Can drop collections and databases if permissions allow
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
audit logging
75
Methodology
Audit logging review
Evidence
MongoDB Audit
Auditing available in MongoDB Enterprise; limited in Community Edition
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09

Privacy & Compliance

66
document data exposure
62
Methodology
Data flow analysis
Evidence
MCP Data Flow
Query results and document data sent to LLM provider
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
pii protection
58
Methodology
PII protection assessment
Evidence
Privacy Analysis
No built-in PII detection or filtering; all queried data exposed
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
field level security
70
Methodology
Field security assessment
Evidence
MongoDB Field-Level Encryption
Supports field-level encryption but requires application-level implementation
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
third party data sharing
68
Methodology
Data sharing analysis
Evidence
LLM Provider Policies
Database content shared with LLM provider per their privacy policy
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
query pattern exposure
72
Methodology
Query privacy assessment
Evidence
Privacy Analysis
Query patterns may reveal data structure and business logic
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09

Trust & Transparency

78
documentation quality
75
Methodology
Documentation completeness review
Evidence
MongoDB MCP Docs
Good documentation but community-maintained with evolving coverage
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
query visibility
80
Methodology
Query logging assessment
Evidence
MCP Protocol
All queries logged in MCP transaction logs
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
open source transparency
88
Methodology
Source code review
Evidence
GitHub Repository
Open source community implementation
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
operation coverage clarity
70
Methodology
API documentation review
Evidence
MCP Server Documentation
Clear but incomplete documentation of supported MongoDB operations
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09

Operational Excellence

80
ease of setup
82
Methodology
Setup complexity assessment
Evidence
Setup Documentation
Simple setup requiring MongoDB connection string
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
query performance
78
Methodology
Performance benchmarking
Evidence
MongoDB Performance
Performance depends on query complexity and indexing (typically 10-500ms)
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
reliability
82
Methodology
Reliability analysis
Evidence
MongoDB Stability
Built on mature MongoDB drivers with high reliability
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
operation coverage
80
Methodology
Feature coverage assessment
Evidence
MongoDB MCP Server
Covers CRUD operations, aggregations, indexing, and collection management
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
community support
75
Methodology
Community support assessment
Evidence
GitHub Community
Community-maintained with moderate activity
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09

✨ Strengths

  • •Comprehensive MongoDB CRUD and aggregation capabilities
  • •Built on official MongoDB drivers with stable performance
  • •Excellent for NoSQL data analysis and document processing
  • •Supports complex aggregation pipelines and queries
  • •Open source community implementation
  • •Flexible schema-less data handling

⚠️ Limitations

  • •Query results and document data exposed to LLM provider
  • •No built-in PII detection or sensitive data filtering
  • •Risk of NoSQL injection without proper query validation
  • •AI can modify or delete data within permission scope
  • •Connection strings with credentials accessible to AI
  • •Limited audit logging in MongoDB Community Edition

📊 Metadata

license: MIT
supported platforms:
0: All platforms with Node.js/Python
programming languages:
0: TypeScript
1: Python
mcp version: 1.0
github repo: https://github.com/modelcontextprotocol/servers
api dependency: MongoDB Node.js Driver, PyMongo
authentication: MongoDB connection string (username/password)
first release: 2024-11
maintained by: Community

Use Case Ratings

code generation

85

Good for generating MongoDB queries and database schemas

customer support

75

Useful for querying customer data and support ticket analysis

content creation

70

Moderate applicability for content management systems using MongoDB

data analysis

92

Excellent for analyzing NoSQL data with aggregation pipelines

research assistant

80

Good for researching data patterns and document structures

legal compliance

55

High risk of exposing sensitive legal documents; requires strict controls

healthcare

50

High risk of HIPAA violations; not recommended without strong data controls

financial analysis

60

Moderate risk; financial data exposure concerns

education

88

Excellent for teaching NoSQL databases and data modeling

creative writing

65

Useful for managing story databases and character information