広告掲載
MCP.so
ログイン

Aleph-10: Vector Memory MCP Server

@bjkemp

リポジトリ

基本情報

カテゴリ

メモリとナレッジ

トランスポート

stdio

作者

bjkemp

Aleph-10: Vector Memory MCP Server について

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

#mathgpt#math-solver#math-assistant

Aleph-10: Vector Memory MCP Server の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

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

Aleph-10: Vector Memory MCP Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

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

よくある質問

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.

コメント

「メモリとナレッジ」の他のコンテンツ

Context7 MCP - Up-to-date Docs For Any Cursor Prompt

upstash

Context7 Platform -- Up-to-date code documentation for LLMs and AI code editors

58.7K

UmmahAPI

Yousuf Kazmi

Islamic source data for assistants: Quran, hadith, tafsir, duas, prayer times and qibla. Answers come from real sources with citations instead of recalled text, so verses and hadith are not hallucinated. 36,000+ hadiths

Azquo

Azquo Holdings Ltd

Exact, shared memory for AI agents: a hierarchical database where totals always add up, every write is attributed to the agent and person behind it, and any figure traces back to its source file. Agents create databases,

Semantic Ui React

Semantic-Org

The official Semantic-UI-React integration

13.2K

M1K3

Round-Tower

Local AI agent for your Mac with a built-in MCP server: 18 tools for your notes and PDFs, a temporal memory graph, an on-device LLM and a neural voice. No account, no telemetry. No data centers required.

7
広告掲載