Moorcheh MCP Server
OfficialClick on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Moorcheh MCP Serversearch my documents for AI ethics"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Quick Start Guide
There are two ways to use the Moorcheh MCP server:
Option 1: NPX (Recommended - No Installation Required)
The easiest way to get started:
# Set your API key and run directly
MOORCHEH_API_KEY=your_api_key_here npx -y @moorchehai/mcpOption 2: Manual Installation
If you prefer to clone and run locally:
Clone the repository
git clone https://github.com/moorcheh-ai/moorcheh-mcp.git cd moorcheh-mcpInstall dependencies
npm install
Step 2: Configure Your API Key
Get your Moorcheh API key
Visit Moorcheh Dashboard
Sign in to your account
Go to your account settings
Generate or copy your API key
Set up your environment
# Copy the example environment file cp env.example .envEdit the .env file
# Open .env in your preferred editor # Replace 'your_moorcheh_api_key_here' with your actual API key MOORCHEH_API_KEY=your_actual_api_key_here
Step 3: Start the Server
npm startThat's it! Your Moorcheh MCP server is now running and ready to use.
Related MCP server: Hashnode Universal MCP Server
Setting Up with Claude Desktop
To use the Moorcheh MCP server with Claude Desktop:
https://github.com/user-attachments/assets/fccbba8e-7393-4b74-8a73-769b55b3f3a3
Step 1: Install Claude Desktop
Download Claude Desktop from https://claude.ai/download
Install and launch Claude Desktop
Step 2: Configure MCP Server
Option A: Using NPX (Recommended)
In Claude Desktop, go to Settings → Developer
Click Edit Config
Configure the server with these settings:
{ "mcpServers": { "moorcheh": { "command": "npx", "args": ["-y", "@moorchehai/mcp"], "env": { "MOORCHEH_API_KEY": "your_actual_api_key_here" } } } }
Option B: Local Installation
In Claude Desktop, go to Settings → Developer
Click Edit Config
Configure the server with these settings:
{ "mcpServers":{ "moorcheh": { "command": "node", "args": [ "path\\to\\moorcheh-mcp\\src\\server\\index.js" ], "env": { "NODE_ENV": "development" } } } }Important:
For Option A: Replace
your_actual_api_key_herewith your actual Moorcheh API keyFor Option B: Replace
path\\to\\moorcheh-mcp\\src\\server\\index.jswith the actual path to yourindex.jsfile and create .env in moorcheh-mcp with your API key
Save the configuration file and restart Claude Desktop completely
Step 3: Test the Connection
Start a new conversation in Claude Desktop
Ask Claude to list the available tools: "Can you list down my namespaces?"
You should see tools like
list-namespaces,search,answer, etc.
Setting Up with Cursor
To use the Moorcheh MCP server with Cursor IDE:
Step 1: Install Cursor
Download Cursor from https://cursor.com
Install and launch Cursor
Step 2: Configure MCP Server
Option A: Using NPX (Recommended)
In Cursor, go to Settings → Tools & integration
Click Add MCP Server
Configure the server with these settings:
{
"mcpServers": {
"moorcheh": {
"command": "npx",
"args": ["-y", "@moorchehai/mcp"],
"env": {
"MOORCHEH_API_KEY": "your_actual_api_key_here"
}
}
}
}Option B: Local Installation
In Cursor, go to Settings → Tools & integration
Click Add MCP Server
Configure the server with these settings:
{
"mcpServers":{
"moorcheh": {
"command": "node",
"args": [
"path\\to\\moorcheh-mcp\\src\\server\\index.js"
],
"env": {
"NODE_ENV": "development"
}
}
}
}Step 3: Set Your API Key
For Option A: Replace
your_actual_api_key_herewith your actual Moorcheh API key in the configurationFor Option B: Create .env in moorcheh-mcp directory and add your API key with
MOORCHEH_API_KEY=your_key_here
Step 4: Test the Connection
Open a new chat in Cursor (Cmd/Ctrl + L)
Ask the AI to list available Moorcheh tools: "What Moorcheh tools can I use?"
You should see tools like
list-namespaces,search,answer, etc.
What This Server Does
The Moorcheh MCP server provides tools for:
Namespace Management: Create, list, and delete namespaces for organizing your data
Document Operations: Upload and manage text documents and vector embeddings
Advanced Search: Perform semantic search across your data
AI-Powered Answers: Get intelligent responses based on your stored data
Available Tools
Namespace Tools
list-namespaces: View all your available namespacescreate-namespace: Create a new namespace for storing datadelete-namespace: Remove a namespace and all its contents
Data Tools
upload-text: Upload text documents to a namespaceupload-vectors: Upload vector embeddings to a namespaceupload-file: Upload files to a text namespace via pre-signed URL (supported types: .pdf, .docx, .xlsx, .json, .txt, .csv, .md)list-files: List raw file objects in S3 storage for a namespace (GET /list-files; see List Files)delete-file: Delete one or more raw files from S3 storage (DELETE /delete-filewithfile_nameand/orfile_names; see Delete File)fetch-text-data: List text and summary chunks from a text namespace (GET/documents/fetch-text-data, up to 100 items per response; see Fetch Text Data)get-data: Retrieve text documents by ID from text namespacesdelete-data: Remove specific data items from a namespace
Search & AI Tools
search: Search across namespaces with vector similarityanswer: Get AI-generated answers based on top of your search
Supported AI models
These IDs match the current Moorcheh Available Models list (REST answer / Python answer.generate).
Model ID | Name | 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
Development
Development Mode
For development with auto-reload:
npm run devAvailable Scripts
Script | Description |
| Start the MCP server |
| Start in development mode with auto-reload |
| Run tests (when available) |
Environment Variables
Variable | Description | Required | Default |
| Your Moorcheh API key | Yes | None |
Troubleshooting
Common Issues
"Missing required API_KEY environment variable"
Make sure you've created a
.envfileVerify your API key is correctly set in the
.envfileCheck that the API key is valid in your Moorcheh dashboard
"Forbidden: Check your API key"
Your API key may be invalid or expired
Generate a new API key from the Moorcheh dashboard
Update your
.envfile with the new key
"Network Error"
Check your internet connection
Verify the API endpoints are accessible
Try again in a few minutes
Getting Help
GitHub Issues: https://github.com/moorcheh-ai/moorcheh-mcp/issues
Moorcheh Documentation: https://console.moorcheh.ai/docs/mcp
Moorcheh Dashboard: https://console.moorcheh.ai
License
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.
Contributing
We welcome contributions! Please feel free to submit a Pull Request.
Changelog
For a detailed list of changes, see CHANGELOG.md.
Available Tools
13 toolsanswerA
Get AI-generated answers based on data in a namespace using text queries. This tool provides intelligent, context-aware responses by searching through your stored text documents and generating comprehensive answers using advanced language models. Supports two modes: Search Mode (with namespace) and Direct AI Mode (empty namespace).
Inputs: namespace (required), query (required), top_k, type, +7 more.
create-namespaceB
Create a new namespace for document storage in Moorcheh
Inputs: namespace_name (required), type, vector_dimension.
delete-dataA
Delete specific data items from a namespace in Moorcheh
Inputs: namespace_name (required), ids (required), data_type.
delete-fileA
Permanently delete file(s) from document storage (S3) for a namespace. Use snake_case: file_name (one file) and/or file_names (array). At least one is required. This deletes storage objects, not indexed documents by pipeline ID (use delete-data for documents/vectors by id).
Inputs: namespace_name (required), file_name, file_names.
delete-namespaceB
Delete a namespace and all its contents from Moorcheh
Inputs: namespace_name (required).
fetch-text-dataA
List text and summary chunks from a text-type namespace via GET /documents/fetch-text-data. Returns up to 100 items per request with statistics (text vs summary counts, source_counts). Only text namespaces are supported; not for vector namespaces. See Moorcheh API docs.
Inputs: namespace_name (required).
get-dataB
Get specific data items by ID from a text namespace in Moorcheh
Inputs: namespace_name (required), ids (required).
list-filesA
List file objects stored in document storage (S3) for a namespace: file_name, size (bytes), last_modified. This is raw storage listing (e.g. after upload-url uploads), not indexed text documents. GET only; no body.
Inputs: namespace_name (required).
list-namespacesA
List all available namespaces in Moorcheh
Inputs: no named parameters.
searchA
Search for data in a namespace using semantic search or vector similarity. This tool provides powerful search capabilities across your namespaces, supporting both text-based semantic search and vector-based similarity search. For text search, you can use natural language queries to find relevant documents based on meaning rather than just keywords. For vector search, you can find similar content by comparing vector embeddings. The tool supports advanced features like result filtering, similarity thresholds, metadata filters, keyword filters, and kiosk mode for production environments. This is ideal for building intelligent search interfaces, recommendation systems, or content discovery features.
Filtering Capabilities:
Metadata Filters: Use #key:value format (e.g., #category:tech, #priority:high)
Keyword Filters: Use #keyword format (e.g., #important, #urgent)
Filters only apply to text search and metadata must be manually uploaded with documents
Inputs: namespaces (required), query (required), query_type, top_k, +2 more.
upload-fileA
Upload a file to a text namespace using pre-signed URL flow. The tool requests an upload URL, uploads the file directly to storage, then the file is queued for processing and indexing.
Inputs: namespace_name (required), file_path (required).
upload-textB
Upload text documents to a namespace in Moorcheh
Inputs: namespace_name (required), documents (required).
upload-vectorsB
Upload vector data to a namespace in Moorcheh
Inputs: namespace_name (required), vectors (required).
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
TDQS
Scored across 13 tools
Most tools have distinct purposes, but 'answer' and 'search' could be confused as both involve querying data. However, descriptions clarify that 'answer' generates responses while 'search' retrieves results.
Tools use snake_case with a verb_noun pattern (e.g., create-namespace, delete-data), but 'answer' is a single verb without a noun, deviating from the pattern.
13 tools cover namespace management, file/document operations, search, and Q&A without being excessive or sparse for the domain.
The set includes CRUD for namespaces and data, but lacks update operations for namespaces and data, and missing a get-file tool for downloading. Minor gaps.
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