Aleph-10: Vector Memory MCP Server
LocalAbout Aleph-10: Vector Memory MCP Server
Aleph-10 is an MCP server for weather lookups and vector-based semantic memory. It supports storing, searching, updating, and deleting memories, with Google Gemini or Ollama embedding options.
Key features
- 01Retrieve weather alerts and forecasts
- 02Store and search vector memories
- 03Update or delete memory entries
- 04Filter memories with metadata
- 05Use Gemini or Ollama embeddings
- 06View memory-store statistics
Use cases
- 01Check weather alerts for a US state
- 02Retrieve a forecast by coordinates
- 03Save information for semantic search
- 04Find memories using metadata filters
MCP support
Exposes weather and vector-memory tools to MCP clients, including forecast retrieval and memory management.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEAleph-10: Vector Memory MCP Server
Aleph-10 is a Model Context Protocol (MCP) server that combines weather data services with vector-based memory storage. This project provides tools for retrieving weather information and managing semantic memory through vector embeddings.
Features
- Weather Information: Get weather alerts and forecasts using the National Weather Service API
- Vector Memory: Store and retrieve information using semantic search
- Multiple Embedding Options: Support for both cloud-based (Google Gemini) and local (Ollama) embedding providers
- Metadata Support: Add and filter by metadata for efficient memory management
Getting Started
Prerequisites
- Node.js 18.x or higher
- pnpm package manager
Installation
- Clone the repository
git clone https://github.com/yourusername/aleph-10.git
cd aleph-10
- Install dependencies
pnpm install
- Configure environment variables (create a
.envfile in the project root)
EMBEDDING_PROVIDER=gemini
GEMINI_API_KEY=your_gemini_api_key
VECTOR_DB_PATH=./data/vector_db
LOG_LEVEL=info
- Build the project
pnpm build
- Run the server
node build/index.js
Usage
The server implements the Model Context Protocol and provides the following tools:
Weather Tools
-
get-alerts: Get weather alerts for a specific US state
- Parameters:
state(two-letter state code)
- Parameters:
-
get-forecast: Get weather forecast for a location
- Parameters:
latitudeandlongitude
- Parameters:
Memory Tools
-
memory-store: Store information in the vector database
- Parameters:
text(content to store),metadata(optional associated data)
- Parameters:
-
memory-retrieve: Find semantically similar information
- Parameters:
query(search text),limit(max results),filters(metadata filters)
- Parameters:
-
memory-update: Update existing memory entries
- Parameters:
id(memory ID),text(new content),metadata(updated metadata)
- Parameters:
-
memory-delete: Remove entries from the database
- Parameters:
id(memory ID to delete)
- Parameters:
-
memory-stats: Get statistics about the memory store
- Parameters: none
Configuration
The following environment variables can be configured:
| Variable | Description | Default |
|---|---|---|
EMBEDDING_PROVIDER | Provider for vector embeddings (gemini or ollama) | gemini |
GEMINI_API_KEY | API key for Google Gemini | - |
OLLAMA_BASE_URL | Base URL for Ollama API | http://localhost:11434 |
VECTOR_DB_PATH | Storage location for vector database | ./data/vector_db |
LOG_LEVEL | Logging verbosity | info |
Development
Project Structure
The project follows a modular structure:
aleph-10/
├── src/ # Source code
│ ├── index.ts # Main application entry point
│ ├── weather/ # Weather service module
│ ├── memory/ # Memory management module
│ ├── utils/ # Shared utilities
│ └── types/ # TypeScript type definitions
├── tests/ # Test files
└── vitest.config.ts # Vitest configuration
Running Tests
The project uses Vitest for testing. Run tests with:
# Run tests once
pnpm test
# Run tests in watch mode during development
pnpm test:watch
# Run tests with UI (optional)
pnpm test:ui
Building
pnpm build
License
This project is licensed under the ISC License.
Acknowledgments
- Model Context Protocol
- National Weather Service API
- Vitest - Next generation testing framework
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.