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MCP Memory Server

Anthropic

76·Strong

Overall Trust Score

MCP server providing AI models with persistent memory and knowledge graph capabilities. Enables long-term information retention, entity relationship tracking, and contextual recall across conversations through the Model Context Protocol. Critical for personalized AI but raises significant privacy concerns.

memory
storage
mcp
model-context-protocol
Version: 2025.9.25
Last Evaluated: November 9, 2025
Official Website →

Trust Vector

Performance & Reliability

83
memory retrieval accuracy
85
Methodology
Retrieval accuracy testing
Evidence
Vector Database Performance
Semantic search with vector embeddings provides good recall
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
knowledge graph integrity
82
Methodology
Graph consistency testing
Evidence
Graph Database Implementation
Entity relationships maintained with reasonable accuracy
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
context recall
80
Methodology
Recall quality assessment
Evidence
Memory System Testing
Can recall past conversations with varying accuracy depending on relevance
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
storage scalability
84
Methodology
Scalability testing
Evidence
Database Backend
Scales with underlying database (SQLite, PostgreSQL, etc.)
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
query performance
81
Methodology
Query latency testing
Evidence
Vector Search Performance
Fast retrieval for small to medium datasets (1-10ms typical)
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09

Security

72
access control
75
Methodology
Access control testing
Evidence
Memory Isolation
User-level memory isolation depends on implementation
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
data modification risk
70
Methodology
Write operation risk assessment
Evidence
Memory Write Operations
AI can modify or delete stored memories
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
memory poisoning risk
68
Methodology
Data integrity risk assessment
Evidence
Security Analysis
AI can store incorrect or malicious information in memory
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
query injection protection
78
Methodology
Injection attack testing
Evidence
Database Query Security
Parameterized queries used but semantic search has different attack vectors
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
audit logging
72
Methodology
Logging capabilities assessment
Evidence
Memory Operations Logging
MCP protocol logs operations but detailed memory audit varies by implementation
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09

Privacy & Compliance

65
pii storage risk
58
Methodology
Privacy risk assessment
Evidence
Memory Content Analysis
Stores personal information, preferences, and conversation history indefinitely
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
data retention control
68
Methodology
Data retention controls review
Evidence
Memory Management
Manual memory deletion possible but no automatic retention policies
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
consent management
62
Methodology
Consent framework review
Evidence
Privacy Controls
No built-in consent mechanisms for memory storage
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
right to deletion
70
Methodology
GDPR right to erasure assessment
Evidence
Memory Deletion
Can delete specific memories but requires manual intervention
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
embedding privacy
65
Methodology
Embedding privacy analysis
Evidence
Vector Embeddings
Text converted to embeddings, sent to embedding provider (OpenAI, etc.)
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09

Trust & Transparency

78
memory visibility
82
Methodology
Memory transparency assessment
Evidence
Memory Inspection
Users can query and view stored memories
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
knowledge graph explainability
75
Methodology
Explainability assessment
Evidence
Graph Visualization
Entity relationships can be viewed but limited visualization tools
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
documentation quality
78
Methodology
Documentation completeness review
Evidence
Documentation
Community documentation with examples but could be more comprehensive
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
open source transparency
80
Methodology
Source code transparency review
Evidence
GitHub Repository
Open source implementation available for review
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09

Operational Excellence

80
ease of setup
82
Methodology
Setup complexity assessment
Evidence
Setup Guide
Straightforward setup with database backend configuration
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
storage efficiency
78
Methodology
Storage efficiency testing
Evidence
Vector Storage
Efficient vector storage with compression options
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
retrieval performance
81
Methodology
Performance benchmarking
Evidence
Query Performance
Fast semantic search for typical workloads
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
maintenance requirements
77
Methodology
Maintenance overhead assessment
Evidence
Database Maintenance
Requires periodic cleanup and optimization
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
backup and recovery
80
Methodology
Backup capabilities review
Evidence
Database Backup
Standard database backup procedures apply
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09

✨ Strengths

  • •Enables true long-term memory and personalization across sessions
  • •Knowledge graph capabilities for entity relationship tracking
  • •Fast semantic search with vector embeddings
  • •Supports building contextual understanding over time
  • •Can dramatically improve AI assistant quality and relevance
  • •Open source implementation with flexibility

⚠️ Limitations

  • •Significant privacy risk - stores personal information indefinitely
  • •No built-in PII detection or automatic data anonymization
  • •Embeddings typically sent to third-party providers (OpenAI, etc.)
  • •Limited consent management and data retention controls
  • •Memory poisoning risk - AI can store incorrect information
  • •GDPR/privacy compliance challenges without careful implementation

📊 Metadata

license: MIT
supported platforms:
0: All platforms with database support
programming languages:
0: TypeScript
1: Python
mcp version: 1.0
github repo: https://github.com/modelcontextprotocol/servers
github stars: 58700
storage backends:
0: SQLite
1: PostgreSQL
2: Redis
3: Vector databases
embedding providers:
0: OpenAI
1: Anthropic
2: Local models
vector dimensions: Varies (384-1536 typical)
first release: 2024-11
maintained by: Anthropic
status: Official - Active
transport types:
0: stdio
installation methods:
0: npm

Use Case Ratings

code generation

78

Useful for remembering coding preferences and project context

customer support

90

Excellent for maintaining customer history and personalized support

content creation

85

Great for maintaining style preferences and project continuity

data analysis

80

Useful for remembering analysis patterns and user preferences

research assistant

88

Excellent for building knowledge graphs and tracking research progress

legal compliance

55

Privacy concerns with storing sensitive case information indefinitely

healthcare

50

High PHI storage risk; retention and consent management challenges

financial analysis

60

Risk of storing sensitive financial information long-term

education

92

Excellent for personalized learning and tracking student progress

creative writing

95

Outstanding for maintaining character details, plot threads, and story continuity