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MCP S3 Server

Community

78·Strong

Overall Trust Score

Community-maintained MCP server for AWS S3 storage operations. Enables AI models to upload, download, list, and manage objects in S3 buckets. Includes support for multipart uploads, object metadata, and bucket operations. Essential for AI-powered cloud storage management and data pipeline workflows.

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

Trust Vector

Performance & Reliability

85
upload reliability
88
Methodology
Upload success rate testing
Evidence
AWS S3 API
Built on reliable AWS S3 API with 99.99% durability SLA
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
download performance
87
Methodology
Download speed testing
Evidence
S3 Performance
High-speed downloads from globally distributed S3 infrastructure
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
large file handling
82
Methodology
Large file transfer testing
Evidence
S3 Multipart Upload
Supports multipart uploads for large files up to 5TB
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
listing performance
80
Methodology
Listing performance testing
Evidence
S3 List Objects
Performance degrades with large buckets; pagination recommended
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
error recovery
86
Methodology
Error handling testing
Evidence
AWS SDK
AWS SDK provides automatic retry with exponential backoff
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09

Security

71
authentication security
80
Methodology
Authentication mechanism review
Evidence
AWS IAM
Uses AWS IAM credentials with fine-grained permissions
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
credential exposure risk
63
Methodology
Credential security analysis
Evidence
MCP Security Model
AWS access keys stored locally; AI can perform S3 operations
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
bucket policy enforcement
78
Methodology
Policy enforcement testing
Evidence
S3 Bucket Policies
Respects S3 bucket policies and IAM permissions
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
object deletion risk
62
Methodology
Destructive operation testing
Evidence
Security Analysis
AI can delete objects and empty buckets if IAM permits
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
public access risk
68
Methodology
Access control testing
Evidence
S3 Public Access
Can modify ACLs and make objects public if permissions allow
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
audit logging
85
Methodology
Audit logging review
Evidence
S3 Server Access Logging
Comprehensive logging via S3 access logs and CloudTrail
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09

Privacy & Compliance

69
object content exposure
65
Methodology
Data flow analysis
Evidence
MCP Data Flow
Downloaded object content and metadata sent to LLM provider
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
metadata privacy
68
Methodology
Metadata privacy assessment
Evidence
Privacy Analysis
Object metadata including tags and user-defined metadata exposed
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
bucket structure exposure
72
Methodology
Structure privacy assessment
Evidence
Privacy Analysis
Bucket names and object keys may reveal organizational structure
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
third party data sharing
70
Methodology
Data sharing analysis
Evidence
LLM Provider Policies
Object content shared with LLM provider per their privacy policy
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
encryption support
75
Methodology
Encryption support assessment
Evidence
S3 Encryption
Supports server-side and client-side encryption but data decrypted for AI
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09

Trust & Transparency

80
documentation quality
78
Methodology
Documentation completeness review
Evidence
S3 MCP Docs
Good documentation but community-maintained with evolving coverage
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
operation visibility
85
Methodology
Logging and traceability assessment
Evidence
AWS CloudTrail
All operations logged in CloudTrail and MCP 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
api coverage clarity
70
Methodology
API documentation review
Evidence
MCP Server Documentation
Clear but incomplete documentation of supported S3 operations
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09

Operational Excellence

83
ease of setup
82
Methodology
Setup complexity assessment
Evidence
Setup Documentation
Requires AWS credentials and appropriate IAM permissions
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
transfer performance
85
Methodology
Performance benchmarking
Evidence
S3 Transfer Acceleration
High-speed transfers with optional transfer acceleration
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
reliability
88
Methodology
Reliability analysis
Evidence
S3 Durability
Built on S3's 99.999999999% durability SLA
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
operation coverage
80
Methodology
Feature coverage assessment
Evidence
S3 MCP Server
Covers upload, download, list, delete, and metadata operations
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

  • •Built on highly reliable AWS S3 with 99.999999999% durability
  • •Comprehensive S3 operations (upload, download, list, delete, metadata)
  • •Excellent for cloud storage automation and data pipelines
  • •Full operation auditability through CloudTrail
  • •Open source community implementation
  • •Supports multipart uploads for large files up to 5TB

⚠️ Limitations

  • •Downloaded object content and metadata exposed to LLM provider
  • •AI can delete objects and modify ACLs within IAM permissions
  • •AWS access keys and credentials accessible to AI
  • •Object metadata and bucket structure may reveal sensitive information
  • •Can make objects public if IAM permissions allow
  • •Encrypted data decrypted before transmission to LLM provider

📊 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: AWS SDK for JavaScript/Python
authentication: AWS IAM credentials (Access Key ID, Secret Access Key)
first release: 2024-11
maintained by: Community

Use Case Ratings

code generation

82

Good for generating S3 integration code and storage workflows

customer support

68

Useful for managing customer file uploads and support attachments

content creation

78

Good for managing media assets and content delivery

data analysis

88

Excellent for data pipeline automation and ETL workflows

research assistant

80

Useful for managing research data and document archives

legal compliance

60

Risk of exposing confidential documents; requires strict IAM controls

healthcare

55

Risk of exposing PHI in stored files; requires careful controls

financial analysis

65

Moderate risk for financial document storage

education

85

Excellent for teaching cloud storage and data pipeline concepts

creative writing

75

Good for managing writing projects and manuscript storage