Aleph‑10: Vector Memory MCP Server

Aleph‑10 bridges two often separate concerns in AI development—real‑time weather data and semantic memory storage. By exposing both a set of weather tools that tap the National Weather Service API and a fully featured vector database, the server lets an assistant answer location‑specific questions while also remembering context across sessions. This dual capability removes the need for separate services or manual data pipelines, simplifying workflows that require up‑to‑date factual information coupled with long‑term knowledge retention.

The server implements the Model Context Protocol, making it immediately compatible with any MCP‑aware assistant. Developers can invoke tools such as get‑alerts or get‑forecast to retrieve alerts and forecasts for a given state or geographic coordinates. In parallel, the memory‑store, memory‑retrieve, and related tools allow an assistant to embed arbitrary text, index it with optional metadata, and perform semantic searches. The vector store supports both cloud‑based (Google Gemini) and local (Ollama) embedding providers, giving teams flexibility to balance cost, latency, or privacy requirements. Metadata filtering on retrieval and statistics queries further enable fine‑grained control over memory usage.

Key capabilities include:

• Real‑time weather insights via the National Weather Service API, supporting alerts and multi‑day forecasts.
• Semantic memory management with vector embeddings that capture meaning beyond keyword matching.
• Hybrid embedding providers, allowing teams to switch between paid cloud models and open‑source local solutions.
• Metadata tagging for efficient pruning, categorization, and targeted retrieval.

Typical use cases span from customer support bots that need to remember user preferences while also checking local weather, to scientific assistants that store research notes and fetch current climate data. In an enterprise setting, the server can serve as a shared knowledge base that multiple assistants consult, ensuring consistency and reducing duplicated effort. The MCP interface means any assistant—Claude, GPT‑4o, or custom agents—can seamlessly call these tools without bespoke integrations.

Aleph‑10’s standout feature is its single‑point solution for two critical AI needs: up‑to‑date factual data and persistent semantic memory. By packaging both under the MCP umbrella, it lowers integration complexity, accelerates development cycles, and opens the door to richer, contextually aware AI experiences.