Verodat

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by Verodat

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What is Verodat

Verodat MCP Server

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Overview

A Model Context Protocol (MCP) server implementation for Verodat, enabling seamless integration of Verodat's data management capabilities with AI systems like Claude Desktop.

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Features

  • Account & Workspace Management

    • List accessible accounts
    • Browse workspaces within accounts
  • Dataset Operations

    • Create datasets with custom schemas and validation
    • Query and filter datasets
    • Retrieve dataset records
  • AI Integration

    • Fetch workspace context for AI processing
    • Execute AI-powered queries on datasets

Prerequisites

  • Node.js (v18 or higher)
  • Git
  • Claude Desktop (for Claude integration)
  • Verodat account and AI API key

Quick Start

Installing via Smithery

To install Verodat MCP Server for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @Verodat/verodat-mcp-server --client claude

Manual Installation

  1. Clone the repository:

    git clone https://github.com/Verodat/verodat-mcp-server.git
    cd verodat-mcp-server
    
  2. Install dependencies and build:

    npm install
    npm run build
    
  3. Configure Claude Desktop:

    Create or modify the config file:

    • MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json
    • Windows: %APPDATA%/Claude/claude_desktop_config.json

    Add the following configuration:

    {
        "mcpServers": {
            "verodat": {
                "command": "node",
                "args": ["path/to/verodat-mcp-server/build/src/index.js"],
                "env": {
                    "VERODAT_AI_API_KEY": "your-verodat-ai-api-key"
                }
            }
        }
    }
    

Getting Started with Verodat

  1. Sign up for a Verodat account at verodat.com
  2. Generate an AI API key from your Verodat dashboard
  3. Add the API key to your Claude Desktop configuration

Available Commands

The server provides the following MCP commands:

// Account & Workspace Management
get-accounts        // List accessible accounts
get-workspaces     // List workspaces in an account
get-queries 		// Retrieve existing AI queries

// Dataset Operations
create-dataset     // Create a new dataset
get-datasets      // List datasets in a workspace
get-dataset-output // Retrieve dataset records
get-dataset-targetfields // Retrieve dataset targetfields
upload-dataset-rows  // Add new data rows to an existing dataset

// AI Operations
get-ai-context     // Get workspace AI context
execute-ai-query   // Run AI queries on datasets

Debugging

The MCP server communicates over stdio, which can make debugging challenging. We provide an MCP Inspector tool to help:

npm run inspector

This will provide a URL to access debugging tools in your browser.

Contributing

We welcome contributions! Please feel free to submit a Pull Request.

License

LICENSE file for details

Support


How to Use

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Frequently Asked Questions

What is MCP?

MCP (Model Context Protocol) is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications, providing a standardized way to connect AI models to different data sources and tools.

What are MCP Servers?

MCP Servers are lightweight programs that expose specific capabilities through the standardized Model Context Protocol. They act as bridges between LLMs like Claude and various data sources or services, allowing secure access to files, databases, APIs, and other resources.

How do MCP Servers work?

MCP Servers follow a client-server architecture where a host application (like Claude Desktop) connects to multiple servers. Each server provides specific functionality through standardized endpoints and protocols, enabling Claude to access data and perform actions through the standardized protocol.

Are MCP Servers secure?

Yes, MCP Servers are designed with security in mind. They run locally with explicit configuration and permissions, require user approval for actions, and include built-in security features to prevent unauthorized access and ensure data privacy.

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