Memory Bank
Production-ready MCP server providing vector-native memory capabilities for AI agents with support for multiple database backends including PostgreSQL with pgvector, Qdrant, MongoDB Atlas Vector Search, and in-memory storage. Features two-tier memory architecture with short-term session buffers and long-term vector storage, shared memory spaces with fine-grained ACL controls and TTL support, and dynamic embedding configuration through AutoEmbedder supporting OpenAI, Gemini, and local models. Exposes comprehensive tools for memory management, contextual retrieval, collaborative spaces, and performance monitoring.
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 2026-09-02.
Scan details: Circle-IR · 2026-09-02 · 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/protocol-lattice/memory-bank-mcp
- Author type
- human
- Last scanned
- 2026-09-02
- Updated
- 2026-09-02
Use via MCP
Resolve Memory Bank 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.