Overview
The Moorcheh MCP Server is a Model Context Protocol (MCP) server that provides seamless integration with Moorcheh's Embedding, Vector Store, Search, and Gen AI Answer services. It enables interaction with Moorcheh's AI capabilities for document embedding, indexing, semantic search, and AI-powered answer generation through the MCP API. The server supports namespace-based data organization, allowing you to create, list, and delete namespaces; upload and manage text documents and vector embeddings; and retrieve or remove data items by ID. It offers advanced search across all namespaces via vector similarity and returns AI-generated responses grounded in your stored data. The MCP server can be run with no installation via NPX or installed locally by cloning the repo and providing a Moorcheh API key stored in a .env file. It exposes tools such as list-namespaces, create-namespace, delete-namespace, upload-text, upload-vectors, get-data, delete-data, search, and answer. It also supports multiple Bedrock models (Claude, Llama) and includes environment configuration guidance for smooth setup.
Features
Seamless Moorcheh integration
Provides a unified interface to Moorcheh's embedding, vector store, search, and AI answer services via MCP.
Namespace management
Create, list, and delete namespaces to organize data efficiently.
Document and vector management
Upload and manage text documents and vector embeddings within namespaces.
Data retrieval and deletion
Retrieve documents by ID and remove specific items from a namespace.
Semantic search
Perform vector-based semantic search across namespaces for relevant results.
AI-powered answers
Generate AI-assisted responses grounded in stored data using Moorcheh's AI capabilities.
Deployment flexibility
Run via NPX with no installation or install locally from source for full control.
Model support
Supports multiple Bedrock models (e.g., Claude, Llama) for inference.
Who Is This For?
- Developers:Integrate embedding, vector storage, and AI answers to build apps.
- Data engineers:Set up namespaces, upload data, and enable semantic search across documents.
- AI teams:Design secure chatbots and RAG systems using Moorcheh MCP capabilities.
