Moorcheh MCP Server
What is MCP?
The Model Context Protocol (MCP) is an open standard that enables AI models to interact with external data sources, tools, and services in a secure and standardized way. It provides a common interface for AI applications to access various resources without needing to understand the specific implementation details of each service. Think of MCP as a universal translator that allows AI models to communicate with different data sources and tools using a common language, regardless of how those services are implemented.Moorcheh MCP Implementation
Our MCP server provides seamless integration with Moorcheh’s comprehensive AI capabilities including document embedding, vector storage, semantic search, and AI-powered answer generation. This server enables you to interact with Moorcheh’s services through the Model Context Protocol.Document Search
Search through your uploaded documents using semantic similarity and natural language queries.
Document Retrieval
Retrieve specific documents by their IDs for direct access to content and metadata.
Namespace Management
Create, list, and delete namespaces for organizing your data efficiently.
AI-Powered Answers
Get intelligent responses based on your stored data using advanced AI models.
Quick Start Guide
There are two ways to use the Moorcheh MCP server:Option 1: NPX (Recommended)
No installation required - run directly with npx
Option 2: Manual Installation
Clone and run locally for development
- Clone the repository
- Install dependencies
- Configure environment
- Start the server
Key Features
Standardization
Consistent interface across different data sources and tools, making integration easier.
Security
API key-based authentication ensures secure access to your data and resources.
Extensibility
Easy to add new data sources and tools without changing your AI application.
Performance
Optimized for efficient data retrieval and processing in AI applications.
Available Tools
Namespace Management
list-namespaces
View all your available namespaces
create-namespace
Create a new namespace for storing data
delete-namespace
Remove a namespace and all its contents
Data Operations
upload-text
Upload text documents to a namespace
upload-file
Directly upload files to a namespace
upload-vectors
Upload vector embeddings to a namespace
get-data
Retrieve documents by ID from text namespaces
delete-data
Remove specific data items from a namespace
Search & AI
search
Search across namespaces with vector similarity
answer
Get AI-generated answers based on your search
Supported AI Models
Our MCP server supports multiple advanced AI models for generating intelligent responses:| Model | Provider | Description |
|---|---|---|
| Claude Sonnet 4.6 | Anthropic | Fast flagship: coding, tools, long docs and RAG (~1M context). |
| Claude Opus 4.6 | Anthropic | Deepest reasoning and hardest tasks; pick when quality matters most (~1M context). |
| Llama 4 Maverick 17B | Meta | Long context, summarization, function calling, fine-tuning friendly. |
| Amazon Nova Pro | Amazon | Chat, math, and structured answers for AWS-style workloads. |
| DeepSeek R1 | DeepSeek | Step-by-step reasoning; math, logic, and technical explanations. |
| DeepSeek V3.2 | DeepSeek | Efficient general Q&A, multilingual, everyday RAG (~164K context). |
| OpenAI GPT OSS 120B | OpenAI | Large generalist: research-style answers and long-form writing. |
| Qwen 3 32B | Qwen | Code and bilingual (EN/ZH) tasks in a smaller footprint. |
| Qwen3 Next 80B A3B | Qwen | MoE model for long chats, docs, and code at scale (~256K context). |
Prerequisites
Node.js
Version 18.0.0 or higher
Moorcheh Account
Active account with API access
Git
For cloning the repository
Next Steps
Setup
Learn how to install, configure, and start the MCP server
Configuration
Understand available tools, supported models, and troubleshooting
Introduction
Deep dive into MCP concepts and Moorcheh’s implementation