MCP Memory Server

v2025.9.25

Anthropic

MCPmemorystoragemcpmodel-context-protocol
76
Strong
About This MCP

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.

Last Evaluated: November 9, 2025
Official Website

Trust Vector Analysis

Dimension Breakdown

🚀Performance & Reliability
+
memory retrieval accuracy

Retrieval accuracy testing

Evidence
Vector Database Performance — Semantic search with vector embeddings provides good recall
mediumVerified: 2025-11-09
knowledge graph integrity

Graph consistency testing

Evidence
Graph Database Implementation — Entity relationships maintained with reasonable accuracy
mediumVerified: 2025-11-09
context recall

Recall quality assessment

Evidence
Memory System Testing — Can recall past conversations with varying accuracy depending on relevance
mediumVerified: 2025-11-09
storage scalability

Scalability testing

Evidence
Database Backend — Scales with underlying database (SQLite, PostgreSQL, etc.)
mediumVerified: 2025-11-09
query performance

Query latency testing

Evidence
Vector Search Performance — Fast retrieval for small to medium datasets (1-10ms typical)
mediumVerified: 2025-11-09
🛡️Security
+
access control

Access control testing

Evidence
Memory Isolation — User-level memory isolation depends on implementation
mediumVerified: 2025-11-09
data modification risk

Write operation risk assessment

Evidence
Memory Write Operations — AI can modify or delete stored memories
mediumVerified: 2025-11-09
memory poisoning risk

Data integrity risk assessment

Evidence
Security Analysis — AI can store incorrect or malicious information in memory
mediumVerified: 2025-11-09
query injection protection

Injection attack testing

Evidence
Database Query Security — Parameterized queries used but semantic search has different attack vectors
mediumVerified: 2025-11-09
audit logging

Logging capabilities assessment

Evidence
Memory Operations Logging — MCP protocol logs operations but detailed memory audit varies by implementation
mediumVerified: 2025-11-09
🔒Privacy & Compliance
+
pii storage risk

Privacy risk assessment

Evidence
Memory Content Analysis — Stores personal information, preferences, and conversation history indefinitely
highVerified: 2025-11-09
data retention control

Data retention controls review

Evidence
Memory Management — Manual memory deletion possible but no automatic retention policies
mediumVerified: 2025-11-09
consent management

Consent framework review

Evidence
Privacy Controls — No built-in consent mechanisms for memory storage
mediumVerified: 2025-11-09
right to deletion

GDPR right to erasure assessment

Evidence
Memory Deletion — Can delete specific memories but requires manual intervention
mediumVerified: 2025-11-09
embedding privacy

Embedding privacy analysis

Evidence
Vector Embeddings — Text converted to embeddings, sent to embedding provider (OpenAI, etc.)
mediumVerified: 2025-11-09
👁️Trust & Transparency
+
memory visibility

Memory transparency assessment

Evidence
Memory Inspection — Users can query and view stored memories
mediumVerified: 2025-11-09
knowledge graph explainability

Explainability assessment

Evidence
Graph Visualization — Entity relationships can be viewed but limited visualization tools
mediumVerified: 2025-11-09
documentation quality

Documentation completeness review

Evidence
Documentation — Community documentation with examples but could be more comprehensive
mediumVerified: 2025-11-09
open source transparency

Source code transparency review

Evidence
GitHub Repository — Open source implementation available for review
highVerified: 2025-11-09
⚙️Operational Excellence
+
ease of setup

Setup complexity assessment

Evidence
Setup Guide — Straightforward setup with database backend configuration
mediumVerified: 2025-11-09
storage efficiency

Storage efficiency testing

Evidence
Vector Storage — Efficient vector storage with compression options
mediumVerified: 2025-11-09
retrieval performance

Performance benchmarking

Evidence
Query Performance — Fast semantic search for typical workloads
mediumVerified: 2025-11-09
maintenance requirements

Maintenance overhead assessment

Evidence
Database Maintenance — Requires periodic cleanup and optimization
mediumVerified: 2025-11-09
backup and recovery

Backup capabilities review

Evidence
Database Backup — Standard database backup procedures apply
mediumVerified: 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

Useful for remembering coding preferences and project context

customer support

Excellent for maintaining customer history and personalized support

content creation

Great for maintaining style preferences and project continuity

data analysis

Useful for remembering analysis patterns and user preferences

research assistant

Excellent for building knowledge graphs and tracking research progress

legal compliance

Privacy concerns with storing sensitive case information indefinitely

healthcare

High PHI storage risk; retention and consent management challenges

financial analysis

Risk of storing sensitive financial information long-term

education

Excellent for personalized learning and tracking student progress

creative writing

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