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Aleph-10: Vector Memory MCP Server

@bjkemp

Repository

Basic information

Category

Memory & Knowledge

Transports

stdio

Author

bjkemp

About Aleph-10: Vector Memory MCP Server

Vector Memory MCP Server - An MCP server with vector-based memory storage capabilities

#mathgpt#math-solver#math-assistant

Aleph-10: Vector Memory MCP Server config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "aleph-10": {
      "command": "node",
      "args": [
        "build/index.js"
      ]
    }
  }
}

Aleph-10: Vector Memory MCP Server tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is Aleph-10: Vector Memory MCP Server?

Aleph-10: Vector Memory MCP Server is a Model Context Protocol server that combines weather data retrieval from the National Weather Service API with vector‑based semantic memory storage. It is intended for developers who need an MCP‑compatible server for both weather information and persistent, searchable memory via embeddings.

How to use Aleph-10: Vector Memory MCP Server?

Install Node.js 18+, pnpm, clone the repository, run pnpm install, configure environment variables in a .env file, then pnpm build and start with node build/index.js. The server exposes tools such as get‑alerts, get‑forecast, memory‑store, memory‑retrieve, memory‑update, memory‑delete, and memory‑stats.

Key features of Aleph-10: Vector Memory MCP Server

  • Retrieves weather alerts and forecasts via the National Weather Service API
  • Stores and retrieves information using semantic vector search
  • Supports both Google Gemini (cloud) and Ollama (local) embedding providers
  • Allows metadata to be attached and filtered on memory entries

Use cases of Aleph-10: Vector Memory MCP Server

  • Fetching live weather alerts for any US state during an MCP session
  • Obtaining weather forecasts for given latitude/longitude coordinates
  • Storing conversational context as vector embeddings for later retrieval
  • Searching for semantically similar text entries across stored memories
  • Updating or deleting individual memory entries by ID

Frequently asked questions

1

What are the prerequisites to run the server?

Node.js 18.x or higher and the pnpm package manager are required.

2

How do I configure the embedding provider?

Set the environment variable `EMBEDDING_PROVIDER` to `gemini` or `ollama`. If using Gemini, provide a `GEMINI_API_KEY`. For Ollama, set `OLLAMA_BASE_URL` (default: `http://localhost:11434`).

3

Where is the vector database stored?

The vector database is stored at the path specified by `VECTOR_DB_PATH`, which defaults to `./data/vector_db`.

4

What weather data source is used?

Weather data is sourced from the National Weather Service API.

5

Can I run embeddings locally without an internet connection?

Yes, by setting `EMBEDDING_PROVIDER` to `ollama` and running a local Ollama instance.

Comments

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