Memory Bank MCP Server

A production-ready Model Context Protocol (MCP) server that provides a powerful, vector-native memory bank for AI agents. Built with the Protocol-Lattice Go Agent Framework, this server offers persistent, searchable, and shareable memory with multiple database backends.

• Server Name: memory-bank-mcp
• Version: 0.1.0

Features

• Multiple Vector Store Backends: Persist memories in your preferred database.

  • In-Memory: For quick tests and ephemeral storage.
  • PostgreSQL: Using the pgvector extension.
  • Qdrant: A dedicated vector database.
  • MongoDB: Using Atlas Vector Search.

• Rich Memory Management:

  • Short-Term Buffer: Temporarily store memories for a session before committing them to long-term storage.
  • Long-Term Persistence: Embed and store memories for semantic retrieval across sessions.
  • Contextual Retrieval: Fetch relevant memories based on a query, combining both short-term and long-term results.

• Shared Memory with "Spaces":

  • Create shared memory "spaces" where multiple agents or users can collaborate.
  • Fine-grained access control (ACLs) with reader, writer, and admin roles.
  • Time-to-live (TTL) support for grants and spaces.

• Dynamic Embeddings: Uses AutoEmbedder from the Go Agent Framework, allowing you to configure the embedding model via environment variables (e.g., OpenAI, Gemini, local models).

Configuration

Configure the server using environment variables.

Memory Store

Set MEMORY_STORE to one of inmemory, postgres, qdrant, or mongo.

• PostgreSQL:

  export MEMORY_STORE="postgres"
  export POSTGRES_DSN="postgres://user:pass@host:port/db?sslmode=disable"
  

• Qdrant:

  export MEMORY_STORE="qdrant"
  export QDRANT_URL="http://localhost:6333"
  export QDRANT_COLLECTION="memories"
  export QDRANT_API_KEY="..." # Optional
  

• MongoDB:

  export MEMORY_STORE="mongo"
  export MONGO_URI="mongodb+srv://..."
  export MONGO_DATABASE="main"
  export MONGO_COLLECTION="memories"
  

Other Settings

• SHORT_TERM_SIZE: Max items in the short-term buffer per session. (Default: 20)
• DEFAULT_SPACE_TTL_SEC: Default TTL for spaces in seconds. (Default: 86400 / 24 hours)

Embedding Model

The server uses AutoEmbedder, which respects ADK_EMBED_* environment variables from the Go Agent Framework. For example, to use Gemini:

export ADK_EMBED_PROVIDER="gemini"
export ADK_EMBED_MODEL="text-embedding-004"
export GEMINI_API_KEY="YOUR_GEMINI_API_KEY"

Quick Start

go install github.com/Protocol-Lattice/memory-bank-mcp@latest
# Run the server (defaults to stdio transport)
memory-bank-mcp

Available MCP Tools

The server exposes a comprehensive set of tools for memory manipulation.

Core Memory

• health.ping: Check if the server is running.
• memory.embed: Get the vector embedding for a piece of text.
• memory.add_short: Add a memory to a session's short-term buffer.
• memory.flush: Persist a session's short-term buffer to the long-term vector store.
• memory.store_long: Directly embed and store a memory in the long-term store.
• memory.retrieve_context: Retrieve relevant memories for a query from a session.

Spaces (Shared Memory)

• spaces.upsert: Create or update a shared space with a TTL and ACL.
• spaces.grant: Grant a role (reader, writer, admin) to a principal for a space.
• spaces.revoke: Revoke a principal's access to a space.
• spaces.list: List all spaces a principal has access to.

Shared Sessions

• shared.join: Make a principal's session view include a specific space.
• shared.leave: Remove a space from a principal's session view.
• shared.add_short_to: Add a short-term memory directly to a shared space.
• shared.retrieve: Retrieve memories from a principal's merged view (local + joined spaces).

MCP Configuration:

{
	"servers": {
    "memory-bank": {
      "command": "/usr/local/bin/memory-bank-mcp",
      "args": ["-transport", "http", "-addr", ":8080"]
    }
	},
	"inputs": []
}

Diagnostics

• engine.metrics: Get a snapshot of the memory engine's performance metrics.