ArangoDB MCP Server

What does “ArangoDB” MCP Server do?

The ArangoDB MCP Server is a TypeScript-based Model Context Protocol (MCP) server that provides seamless database interaction capabilities with ArangoDB. Acting as a powerful bridge between AI assistants and the ArangoDB database, it enables developers and LLM-powered agents to execute core database operations directly through standardized MCP tools. This server allows tasks such as querying data, inserting and updating documents, managing collections, and performing backups, all via simple tool calls. Its integration with platforms like Claude, VSCode (via extensions such as Cline), and more, makes it a versatile backend for enhancing development workflows that require real-time or programmatic access to structured data.

List of Prompts

No prompt templates are specifically mentioned in the repository.

List of Resources

No explicit MCP resources are described in the repository documentation.

List of Tools

• arango_query
  Execute AQL (ArangoDB Query Language) queries. Takes a query string and optional bind variables, returning query results as JSON.

• arango_insert
  Insert documents into collections. Requires collection name and document object; auto-generates document key if not provided.

• arango_update
  Update existing documents in a collection. Requires collection name, document key, and update object.

• arango_remove
  Remove documents from collections. Requires collection name and document key.

• arango_backup
  Backup all collections to JSON files in a specified directory, helpful for data migration and backup.

• arango_list_collections
  List all collections in the database, returning their names, IDs, and types.

• arango_create_collection
  Create a new collection (document or edge type), with options for name, type, and sync behavior.

Use Cases of this MCP Server

• Database Management
  Easily perform CRUD operations (create, read, update, delete) on ArangoDB collections directly from AI-powered tools or chat agents.

• Codebase Exploration for Data-Driven Projects
  Enable developers to query and modify application data without leaving their development environment, streamlining data exploration.

• Automated Data Backups and Migration
  Use the backup tool to export collection data as JSON, supporting automated backup workflows and facilitating migrations.

• Integration with AI Agents
  Empower LLMs and AI assistants (like Claude or those in VSCode via Cline) to fetch, update, or analyze database data as part of agentic workflows.

• Dynamic Collection Management
  Allow programmatic creation and listing of collections, supporting rapid prototyping or multi-tenant applications.

How to set it up

Windsurf

No setup instructions provided for Windsurf.

Claude

No setup instructions provided for Claude.

Cursor

No setup instructions provided for Cursor.

Cline (VSCode extension)

1. Prerequisites: Ensure you have VSCode (version 1.99.0 or above) installed.
2. Edit/Create MCP config:
   • Workspace: .vscode/mcp.json
   • User: Add to VSCode user settings for global use.
3. Add the ArangoDB MCP server configuration:

   {
     "servers": {
       "arango-mcp": {
         "type": "stdio",
         "command": "npx",
         "args": ["arango-server"],
         "env": {
           "ARANGO_URL": "http://localhost:8529",
           "ARANGO_DB": "v20",
           "ARANGO_USERNAME": "app",
           "ARANGO_PASSWORD": "75Sab@MYa3Dj8Fc"
         }
       }
     }
   }
   

4. Start the MCP server:
   • Open Command Palette (Ctrl+Shift+P or Cmd+Shift+P on Mac)
   • Run MCP: Start Server and select arango-mcp.

Securing API Keys

It’s recommended to use environment variables for credentials. Example:

{
  "servers": {
    "arango-mcp": {
      "type": "stdio",
      "command": "npx",
      "args": ["arango-server"],
      "env": {
        "ARANGO_URL": "${env:ARANGO_URL}",
        "ARANGO_DB": "${env:ARANGO_DB}",
        "ARANGO_USERNAME": "${env:ARANGO_USERNAME}",
        "ARANGO_PASSWORD": "${env:ARANGO_PASSWORD}"
      }
    }
  }
}

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "arango-mcp": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “arango-mcp” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Our opinion

This MCP server is well-documented for tool support and setup, especially for VSCode/Cline. However, it lacks explicit prompt templates and resource descriptions, and there is no mention of sampling or roots support. Its main strength is a comprehensive set of database tools and clear environment variable handling.
Overall, we rate this MCP server a 7/10 for practical utility and documentation, but missing some advanced MCP features.

MCP Score