Vertica MCP Server

What does “Vertica” MCP Server do?

The Vertica MCP (Model Context Protocol) Server is designed to facilitate seamless integration between AI assistants and the Vertica (OpenText Vertica) database system. Acting as a bridge, it allows AI clients to execute complex database operations, manage schemas, and interact with large datasets efficiently. With features such as connection pooling, SSL/TLS security, and granular permission controls, the Vertica MCP Server enables tasks such as executing SQL queries, streaming query results in batches, inspecting database schemas, and managing indexes and views. This server significantly streamlines the workflow for developers and data engineers who need to interface AI tools with enterprise-grade Vertica databases, supporting use cases like automated data analysis, reporting, and real-time data processing.

List of Prompts

No prompt templates are explicitly mentioned in the provided repository documentation.

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List of Resources

No explicit MCP resources are documented in the repository.

List of Tools

• execute_query
  Execute SQL queries with support for all SQL operations.

• stream_query
  Stream large query results in batches, with configurable batch sizes for efficient data handling.

• copy_data
  Perform bulk data loading using Vertica’s COPY command, suitable for large datasets.

• get_table_structure
  Retrieve detailed table structures, including column information and constraints.

• list_indexes
  List all indexes for a specified table, along with index types, uniqueness, and related columns.

• list_views
  List all views within a schema and provide their definitions.

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Use Cases of this MCP Server

• Database Query Automation
  AI agents can execute complex SQL queries on Vertica databases, enabling automated data retrieval and report generation.

• Bulk Data Ingestion
  Efficiently load large datasets into Vertica using the COPY command, supporting big data workflows and ETL processes.

• Schema and Structure Inspection
  Developers can automatically inspect table structures, indexes, and views to understand and document database schemas.

• Real-time Data Streaming
  Stream large query results in manageable batches, facilitating scalable analytics and real-time monitoring dashboards.

• Secure and Permissioned Access
  Enforce granular operation and schema-level permissions for sensitive data operations, ensuring compliance and security in enterprise environments.

How to set it up

Windsurf

1. Ensure you have Node.js and the uvx runtime installed.
2. Locate your Windsurf configuration file.
3. Add the Vertica MCP server with the following JSON snippet:

   {
     "mcpServers": {
       "vertica": {
         "command": "uvx",
         "args": [
           "mcp-vertica",
           "--host=localhost",
           "--db-port=5433",
           "--database=VMart",
           "--user=dbadmin",
           "--password=",
           "--connection-limit=10"
         ]
       }
     }
   }
   

4. Save the configuration and restart Windsurf.
5. Verify the server connection via the Windsurf interface.

Securing API Keys (Environment Variables)

{
  "mcpServers": {
    "vertica": {
      "command": "uvx",
      "args": ["mcp-vertica"],
      "env": {
        "VERTICA_HOST": "localhost",
        "VERTICA_PORT": 5433,
        "VERTICA_DATABASE": "VMart",
        "VERTICA_USER": "dbadmin",
        "VERTICA_PASSWORD": "",
        "VERTICA_CONNECTION_LIMIT": 10,
        "VERTICA_SSL": false,
        "VERTICA_SSL_REJECT_UNAUTHORIZED": true
      }
    }
  }
}

Claude

1. Install Node.js and uvx.
2. Open the Claude config file.
3. Add the Vertica MCP server as shown above.
4. Save changes and restart Claude.
5. Confirm the server is active in Claude’s UI.

Cursor

1. Install required dependencies (Node.js, uvx).
2. Edit Cursor’s configuration file.
3. Insert the Vertica MCP server configuration JSON.
4. Save and restart Cursor.
5. Check connection status in Cursor’s dashboard.

Cline

1. Prepare your environment with Node.js and uvx.
2. Access the Cline MCP config.
3. Add the Vertica MCP server block as per the JSON example.
4. Save and restart Cline.
5. Validate connection within Cline.

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:

{
  "vertica": {
    "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 “vertica” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

A solid, focused MCP server for Vertica with strong tooling for database ops, but missing prompt templates, explicit resource definitions, root boundaries, and sampling support. Good security and setup documentation. Rating: 6/10.

MCP Score