by Verodat
Enables AI models to interact with Verodat's data management capabilities through a set of standardized tools for retrieving, creating, and managing datasets.
Provides a Model Context Protocol (MCP) server that exposes Verodat's data handling functions—such as listing accounts, workspaces, datasets, and executing AI‑powered queries—to AI models. The server is organized into three tool categories (Consume, Design, Manage) that progressively add write capabilities.
npx -y @smithery/cli install @Verodat/verodat-mcp-server --client claude
consume.js, design.js, or manage.js).VERODAT_AI_API_KEY) and optionally override the API base URL (VERODAT_API_BASE_URL).npm install and build.Q: Which tool set should I use?
A: Choose consume.js for read‑only operations, design.js when you need to create datasets, and manage.js for full upload capabilities.
Q: Do I need a Verodat account? A: Yes—sign up at verodat.com and generate an AI API key from the dashboard.
Q: How do I change the API endpoint?
A: Set the VERODAT_API_BASE_URL environment variable; it defaults to https://verodat.io/api/v3.
Q: Can I debug the MCP communication?
A: Run npm run inspector to launch the MCP Inspector web UI.
Q: Is the server compatible with other AI models? A: While the README focuses on Claude Desktop, any model that supports MCP over stdio can use the server.
A Model Context Protocol (MCP) server implementation for Verodat, enabling seamless integration of Verodat's data management capabilities with AI systems like Claude Desktop.
This repository contains a Model Context Protocol (MCP) server implementation for Verodat, allowing AI models to interact with Verodat's data management capabilities through well-defined tools.
The Verodat MCP Server provides a standardized way for AI models to access and manipulate data in Verodat. It implements the Model Context Protocol specification, providing tools for data consumption, design, and management.
The server is organized into three main tool categories, each offering a progressive set of capabilities:
The base category focused on data retrieval operations:
get-accounts: Retrieve available accountsget-workspaces: List workspaces within an accountget-datasets: List datasets in a workspaceget-dataset-output: Retrieve actual data from a datasetget-dataset-targetfields: Retrieve field definitions for a datasetget-queries: Retrieve existing AI queriesget-ai-context: Get workspace context and data structureexecute-ai-query: Execute AI-powered queries on datasetsIncludes all tools from Consume, plus:
create-dataset: Create a new dataset with defined schemaIncludes all tools from Design, plus:
upload-dataset-rows: Upload data rows to existing datasetsTo install Verodat MCP Server for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @Verodat/verodat-mcp-server --client claude
git clone https://github.com/Verodat/verodat-mcp-server.git
cd verodat-mcp-server
npm install
npm run build
Configure Claude Desktop: Create or modify the config file:
~/Library/Application Support/Claude/claude_desktop_config.json%APPDATA%/Claude/claude_desktop_config.jsonAdd the configuration which is mensioned below in configuration:
The server requires configuration for authentication and API endpoints. Create a configuration file for your AI model to use:
{
"mcpServers": {
"verodat-consume": {
"command": "node",
"args": [
"path/to/verodat-mcp-server/build/src/consume.js"
],
"env": {
"VERODAT_AI_API_KEY": "your-api-key",
"VERODAT_API_BASE_URL": "https://verodat.io/api/v3"
}
}
}
}
You can configure any of the three tool categories by specifying the appropriate JS file one at a time in claude:
consume.js (8 tools for data retrieval)design.js (9 tools, includes dataset creation)manage.js (10 tools, includes data upload)Example for configuring all three categories simultaneously:
{
"mcpServers": {
"verodat-consume": {
"command": "node",
"args": [
"path/to/verodat-mcp-server/build/src/consume.js"
],
"env": {
"VERODAT_AI_API_KEY": "your-api-key",
"VERODAT_API_BASE_URL": "https://verodat.io/api/v3"
}
},
"verodat-design": {
"command": "node",
"args": [
"path/to/verodat-mcp-server/build/src/design.js"
],
"env": {
"VERODAT_AI_API_KEY": "your-api-key",
"VERODAT_API_BASE_URL": "https://verodat.io/api/v3"
}
},
"verodat-manage": {
"command": "node",
"args": [
"path/to/verodat-mcp-server/build/src/manage.js"
],
"env": {
"VERODAT_AI_API_KEY": "your-api-key",
"VERODAT_API_BASE_URL": "https://verodat.io/api/v3"
}
}
}
}
VERODAT_AI_API_KEY: Your Verodat API key for authenticationVERODAT_API_BASE_URL: The base URL for the Verodat API (defaults to "https://verodat.io/api/v3" if not specified)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
consume.js server configurationdesign.js server configurationmanage.js server configurationThe codebase is written in TypeScript and organized into:
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.
We welcome contributions! Please feel free to submit a Pull Request.
LICENSE file for details
Please log in to share your review and rating for this MCP.
{
"mcpServers": {
"verodat-consume": {
"command": "npx",
"args": [
"-y",
"@verodat/verodat-mcp-server",
"consume"
],
"env": {
"VERODAT_AI_API_KEY": "<YOUR_API_KEY>",
"VERODAT_API_BASE_URL": "https://verodat.io/api/v3"
}
},
"verodat-design": {
"command": "npx",
"args": [
"-y",
"@verodat/verodat-mcp-server",
"design"
],
"env": {
"VERODAT_AI_API_KEY": "<YOUR_API_KEY>",
"VERODAT_API_BASE_URL": "https://verodat.io/api/v3"
}
},
"verodat-manage": {
"command": "npx",
"args": [
"-y",
"@verodat/verodat-mcp-server",
"manage"
],
"env": {
"VERODAT_AI_API_KEY": "<YOUR_API_KEY>",
"VERODAT_API_BASE_URL": "https://verodat.io/api/v3"
}
}
}
}claude mcp add verodat-consume npx -y @verodat/verodat-mcp-server consumeExplore related MCPs that share similar capabilities and solve comparable challenges
by DMontgomery40
A Model Context Protocol server that proxies DeepSeek's language models, enabling seamless integration with MCP‑compatible applications.
by deepfates
Runs Replicate models through the Model Context Protocol, exposing tools for model discovery, prediction management, and image handling via a simple CLI interface.
by 66julienmartin
A Model Context Protocol (MCP) server implementation connecting Claude Desktop with DeepSeek's language models (R1/V3)
by ruixingshi
Provides Deepseek model's chain‑of‑thought reasoning to MCP‑enabled AI clients, supporting both OpenAI API mode and local Ollama mode.
by groundlight
Expose HuggingFace zero‑shot object detection models as tools for large language or vision‑language models, enabling object localisation and zoom functionality on images.
by 66julienmartin
Provides a Model Context Protocol server for the Qwen Max language model, enabling seamless integration with Claude Desktop and other MCP‑compatible clients.
Run advanced AI models locally with high performance while maintaining full data privacy, accessible through native desktop applications and a browser‑based platform.
Upload, analyze, and visualize documents, compare multiple AI model responses side‑by‑side, generate diagrams, solve math with KaTeX, and collaborate securely within a single unified interface.
by netdata
Delivers real‑time, per‑second infrastructure monitoring with zero‑configuration agents, on‑edge machine‑learning anomaly detection, and built‑in dashboards.