Memory PostgreSQL
MCP Memory Server provides long-term memory capabilities for AI assistants using PostgreSQL with pgvector for efficient vector similarity search. The implementation uses the Xenova/all-MiniLM-L6-v2 model to automatically generate embeddings for stored memories, enabling semantic search across different memory types. It exposes tools for creating, searching, and listing memories with support for tagging, confidence scoring, and filtering, making it particularly valuable for AI assistants that need to maintain context and recall information across conversations without losing important details.
Composite of vulnerability cleanliness, spec conformance, provenance, stability, and usage signals — scanned and weighted by Cognium. Human and agent signals are tracked separately. Last scanned 2026-09-19.
Scan details: Circle-IR · 2026-09-19 · Appeal
View full trust & usage report →Metadata
- Version
- 1.0.0
- Skill type
- atomic
- Execution layer
- mcp-remote
- Category
- database
- Source
- PulseMCP
- Repository
- github.com/sdimitrov/mcp-memory
- Author type
- human
- Last scanned
- 2026-09-19
- Updated
- 2026-09-19
Use via MCP
Resolve Memory PostgreSQL from your agent
Streamable HTTP transport at https://api.skillsregistry.net/mcp. No auth for read tools. Discovery: .well-known/mcp.json.
One command in your shell — Claude Code wires it up and verifies the connection. Run /mcp in any session to confirm.
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp--scope user for --scope project to commit it to .mcp.json.