Which integrations are available for this server?

Provides instructions for running Vertica with Docker Compose, including environment setup and container configuration for testing purposes. Supports configuration through environment variables files, allowing users to set connection parameters, security settings, and operation permissions without command-line arguments.

How do I use MCP Vertica?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@MCP Vertica show me the top 10 customers by total sales this month" 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.

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MCP Vertica

Q: What can you do with this server?

MCP Vertica is a Model Context Protocol server that provides comprehensive database connectivity and management tools for Vertica databases. * Execute SQL queries - Run any SQL operations including SELECT, INSERT, UPDATE, DELETE statements * Stream large query results - Process query results in configurable batches for efficient handling of large datasets * Bulk data operations - Use COPY command for efficient bulk data loading into Vertica tables * Schema inspection - Get detailed table structures including columns, data types, and constraints * Index and view management - List all indexes for tables and views within schemas along with their definitions * Connection management - Configurable connection pooling with SSL/TLS support, automatic cleanup, and timeout handling * Security controls - Operation-level permissions (INSERT, UPDATE, DELETE, DDL) and schema-specific access controls * Configuration options - Set database connection parameters (host, port, database, user, password, connection limits) and enable debug logging for troubleshooting

by nolleh

Overview Schema Source Related Servers Discussions Admin

Python

Remote

MCP Vertica

PyPI version License: MIT Downloads MCP Community

🏆 First implementation of Vertica MCP Server • Learn more

✅ Listed in Model Context Protocol Official Registry

A Vertica MCP(model-context-protocol) Server

Example: MCP Server Setting

Create or edit the file your mcp client config file with the following content:

UVX

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

Or with args

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

Docker

{
  "mcpServers": {
    "vertica": {
      "command": "docker",
      "args": ["run", "-i", "--rm", "nolleh/mcp-vertica"],
      "env": {
        "VERTICA_HOST": "localhost",
        "VERTICA_PORT": 5433,
        "VERTICA_DATABASE": "VMart",
        "VERTICA_USER": "dbadmin",
        "VERTICA_PASSWORD": "test_password",
        "VERTICA_CONNECTION_LIMIT": 10,
        "VERTICA_SSL": false,
        "VERTICA_SSL_REJECT_UNAUTHORIZED": true
      }
    }
  }
}

[!Note]
For boolean flags like --ssl or --ssl-reject-unauthorized, simply add the flag (e.g., "--ssl") to enable it, or omit it to disable.
For an empty password, use an empty string as shown above.

Features

Database Connection Management

Connection pooling with configurable limits
SSL/TLS support
Automatic connection cleanup
Connection timeout handling

Query Operations

Execute SQL queries
Stream large query results in batches
Copy data operations
Transaction management

Schema Management

Table structure inspection
Index management
View management
Constraint information
Column details

Security Features

Operation-level permissions (INSERT, UPDATE, DELETE, DDL)
Schema-specific permissions
SSL/TLS support
Password masking in logs

Related MCP server: MSSQL MCP Server

Tools

Database Operations

execute_query
- Execute SQL queries
- Support for all SQL operations
stream_query
- Stream large query results in batches
- Configurable batch size
copy_data
- Bulk data loading using COPY command
- Efficient for large datasets

Schema Management

get_table_structure
- Get detailed table structure
- Column information
- Constraints
list_indexes
- List all indexes for a table
- Index type and uniqueness
- Column information
list_views
- List all views in a schema
- View definitions

Configuration

Environment Variables

VERTICA_HOST=localhost
VERTICA_PORT=5433
VERTICA_DATABASE=VMart
VERTICA_USER=newdbadmin
VERTICA_PASSWORD=vertica
VERTICA_CONNECTION_LIMIT=10
VERTICA_SSL=false
VERTICA_SSL_REJECT_UNAUTHORIZED=true

Operation Permissions

ALLOW_INSERT_OPERATION=false
ALLOW_UPDATE_OPERATION=false
ALLOW_DELETE_OPERATION=false
ALLOW_DDL_OPERATION=false

Schema Permissions

SCHEMA_INSERT_PERMISSIONS=schema1:true,schema2:false
SCHEMA_UPDATE_PERMISSIONS=schema1:true,schema2:false
SCHEMA_DELETE_PERMISSIONS=schema1:true,schema2:false
SCHEMA_DDL_PERMISSIONS=schema1:true,schema2:false

Installation

Installing via Smithery

To install Vertica Database Connector for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @nolleh/mcp-vertica --client claude

Installing Manually

Open your favorite mcp client's config file, then configure with uvx mcp-vertica

Example: Mcp Server Setting

Development

Debug Mode

When running with Docker, you can enable debug logging by setting the DEBUG environment variable:

# Run with maximum verbosity (-vvv)
docker run -e DEBUG=3 -e VERTICA_HOST=localhost ... nolleh/mcp-vertica:latest

# Run with medium verbosity (-vv)
docker run -e DEBUG=2 -e VERTICA_HOST=localhost ... nolleh/mcp-vertica:latest

# Pass additional arguments
docker run -e EXTRA_ARGS="--connection-limit=20" -e VERTICA_HOST=localhost ... nolleh/mcp-vertica:latest

In docker-compose.yml:

environment:
  DEBUG: 3  # 0=none, 1=-v, 2=-vv, 3=-vvv
  EXTRA_ARGS: "--connection-limit=20"  # Optional additional arguments

Appendix: For Testing, VerticaDB Docker Compose Example

version: "3.8"

services:
  vertica:
    # image: vertica/vertica-ce:11.1.0-0
    image: vertica/vertica-ce:latest
    platform: linux/amd64
    container_name: vertica-ce
    environment:
      VERTICA_MEMDEBUG: 2
    ports:
      - "5433:5433"
      - "5444:5444"
    volumes:
      - vertica_data:/home/dbadmin/VMart
    healthcheck:
      test:
        [
          "CMD",
          "/opt/vertica/bin/vsql",
          "-h",
          "localhost",
          "-d",
          "VMart",
          "-U",
          "dbadmin",
          "-c",
          "SELECT 1",
        ]
      interval: 10s
      timeout: 5s
      retries: 5
      start_period: 30s
    restart: unless-stopped

  mcp-vertica:
    image: nolleh/mcp-vertica:latest
    container_name: mcp-vertica
    ports:
      - "8081:8081"
    environment:
      # Transport mode
      TRANSPORT: http
      PORT: 8081
      # Debug settings (0=none, 1=-v, 2=-vv, 3=-vvv)
      DEBUG: 3  # Set to 3 for maximum verbosity
      # Extra command line arguments (optional)
      # EXTRA_ARGS: "--some-flag"
      # Vertica connection settings
      VERTICA_HOST: vertica
      VERTICA_PORT: 5433
      VERTICA_DATABASE: VMart
      VERTICA_USER: dbadmin
      VERTICA_PASSWORD: ""
      VERTICA_CONNECTION_LIMIT: 10
      VERTICA_SSL: "false"
    depends_on:
      vertica:
        condition: service_healthy

volumes:
  vertica_data:
    driver: local

Then run server by following instruction Example: Mcp Server Setting, Then see everything works as fine

License

This project is licensed under the MIT License - see the LICENSE file for details.

Available Tools

6 tools

copy_dataC

Copy data into a Vertica table using COPY command.

Args:
    ctx: FastMCP context for progress reporting and logging
    schema: vertica schema to execute the copy against
    table: Target table name
    data: List of rows to insert

Returns:
    Status message indicating success or failure

ParametersJSON Schema

Name	Required	Description	Default
`schema`	Yes
`table`	Yes
`data`	Yes

TDQS

C2.9/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden. It mentions using COPY command but lacks details on permissions, error handling, or side effects. Returns a status message but no specifics.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is concise with Args and Returns sections, front-loading the purpose. No unnecessary sentences.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, no annotations, and 3 required params, the description is incomplete. It doesn't mention table existence requirements, data format constraints, or behavior on conflict.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, but description adds minimal meaning: 'vertica schema', 'Target table name', 'List of rows to insert'. The 'data' parameter lacks structure details beyond list of rows, leaving ambiguity.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it copies data into a Vertica table using COPY command, which is a specific verb and resource. It is easily distinguishable from siblings like execute_query or list_indexes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool vs alternatives like execute_query for single inserts. No prerequisites or exclusions are mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

execute_queryC

Execute a SQL query and return the results.

Args:
    ctx: FastMCP context for progress reporting and logging
    query: SQL query to execute
    database: Optional database name to execute the query against

Returns:
    Query results as a string

ParametersJSON Schema

Name	Required	Description	Default
`query`	Yes

TDQS

C2.7/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description must disclose behavioral traits. It only states the action and return type, omitting crucial details such as whether the query is read-only, impacts on data, or required permissions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is reasonably concise with a clear structure (Args, Returns). The purpose sentence is front-loaded, and every sentence serves a purpose despite the parameter mismatch.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given that there is no output schema and no annotations, the description is insufficient. It lacks details about error handling, supported SQL syntax, database selection, and whether the tool is safe for read-only queries.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds meaning for the 'query' parameter ('SQL query to execute'), but it also mentions 'ctx' and 'database' which are not in the input schema, causing confusion. Schema description coverage is 0%, so the description should compensate but instead introduces inconsistency.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Execute a SQL query and return the results,' which is a specific verb and resource. However, the mention of parameters not present in the input schema (ctx, database) slightly reduces clarity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus siblings like stream_query or copy_data. The description does not specify prerequisites or alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_table_structureB

Get the structure of a table including columns, data types, and constraints.

Args:
    ctx: FastMCP context for progress reporting and logging
    table_name: Name of the table to inspect
    schema: Schema name (default: public)

Returns:
    Table structure information as a string

ParametersJSON Schema

Name	Required	Description	Default
`table_name`	Yes
`schema`	No		public

TDQS

B3.1/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must fully disclose behavior. It mentions returning 'Table structure information as a string' but does not describe the format, side effects (e.g., read-only), error behavior, or performance characteristics.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is relatively concise, with the purpose stated in the first line. However, the Args/Returns block adds some verbosity without significant extra value, and could be streamlined.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema and a potentially complex return value, the description should elaborate on the format of the returned string (e.g., JSON, plain text). It also lacks error handling details and examples.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description adds value beyond the schema. It defines table_name and schema with a default, but does not provide examples, constraints (e.g., required format for table_name), or clarification of schema parameter semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool retrieves table structure including columns, data types, and constraints. It uses a specific verb (Get) and resource (table structure), and is distinct from sibling tools like execute_query or list_views.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description does not provide any guidance on when to use this tool versus alternatives. No mention of prerequisites, cases where other tools are preferred, or scope limitations.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

list_indexesB

List all indexes for a specific table.

Args:
    ctx: FastMCP context for progress reporting and logging
    table_name: Name of the table to inspect
    schema: Schema name (default: public)

Returns:
    Index information as a string

ParametersJSON Schema

Name	Required	Description	Default
`table_name`	Yes
`schema`	No		public

TDQS

B3/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are present, so the description must disclose behavioral traits. It states returns are a string but omits details on performance, side effects, permissions, or error handling, leaving the agent under-informed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is short and includes an Args/Returns structure, making it reasonably scannable. It could be slightly more concise by merging the header, but overall it is efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 2 parameters and no output schema, the description is minimal. It lacks details on the return format, edge cases, or database prerequisites, making it only barely adequate for operation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, but the description adds meaningful parameter descriptions ('Name of the table to inspect' and 'Schema name (default: public)'), compensating for the lack of schema descriptions. This adds value beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'List all indexes for a specific table,' specifying the verb and resource. However, it does not differentiate from sibling tools like get_table_structure, which might have similar functionality.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternative tools. The description only states what the tool does, without any when-not or alternative references.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

list_viewsB

List all views in a schema.

Args:
    ctx: FastMCP context for progress reporting and logging
    schema: Schema name (default: public)

Returns:
    View information as a string

ParametersJSON Schema

Name	Required	Description	Default
`schema`	No		public

TDQS

B3.3/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description must disclose behavioral traits. It mentions returning a string but does not reveal side effects, authentication needs, or performance implications. The included 'ctx' parameter is not in the input schema, causing inconsistency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is short and uses a structured 'Args/Returns' format. However, it includes redundant technical formatting and a parameter (ctx) that does not appear in the schema, which could confuse an agent.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Missing important details: the return format of the string, progress reporting behavior (despite mentioning ctx), and how to handle large results. The discrepancy between description params and schema further reduces completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% (low), so the description must compensate. The description provides meaning for the 'schema' parameter ('Schema name (default: public)'), adding semantic value beyond the schema's default and type. However, it includes an undocumented 'ctx' parameter, slightly reducing clarity.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states 'List all views in a schema,' clearly identifying the resource (views) and action (list). It distinguishes from sibling tools like list_indexes and get_table_structure by specifying it is for views.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives. It does not mention prerequisites, exclusions, or comparison with siblings. The description only states what it does, not the context for use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

stream_queryC

Execute a SQL query and return the results in batches as a single string.

Args:
    ctx: FastMCP context for progress reporting and logging
    query: SQL query to execute
    batch_size: Number of rows to fetch at once

Returns:
    Query results as a concatenated string

ParametersJSON Schema

Name	Required	Description	Default
`query`	Yes
`batch_size`	No

TDQS

C2.9/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description should fully disclose behavioral traits. It mentions batching and progress reporting but omits critical details like whether the tool modifies data, safety concerns, or authentication requirements. The description does not warn about potentially destructive SQL operations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise with a single-purpose summary and structured Args/Returns. No extraneous text, though it could be more informative without losing conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (SQL execution with optional batching, no output schema) and zero annotations, the description is incomplete. It fails to explain return value format, error handling, or when to adjust batch_size. The agent lacks sufficient context to use it safely.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description must add meaning. It provides minimal descriptions for 'query' and 'batch_size' (e.g., 'SQL query to execute') but lacks constraints, valid ranges, or examples. The batch_size default and meaning are implied but not elaborated.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Execute a SQL query') and the unique output format ('return the results in batches as a single string'). This distinguishes it from siblings like execute_query, which likely returns results differently.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives (e.g., execute_query). There is no mention of prerequisites, limitations, or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

6 tool updatesAug 15, 2025
- First observedcopy_data
- First observedexecute_query
- First observedget_table_structure
- First observedlist_indexes
- First observedlist_views
- First observedstream_query

Deploy Server

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

B3.1/5.0

Scored 2026-05-02 06:25 across 6 tools

Disambiguation3/5

Most tools are distinct, but 'execute_query' and 'stream_query' both execute SQL queries with only a difference in batch handling, leading to potential confusion for an agent.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern with underscores (e.g., copy_data, list_indexes, get_table_structure), making the naming predictable.

Tool Count4/5

6 tools is a reasonable size for a focused database server, though it leans slightly low.

Completeness2/5

The set covers query execution and data copying but lacks essential operations like table creation, modification, and deletion, leaving significant gaps for typical database management.

Maintenance

ActivityInactive

ResponsivenessUnresponsive

Resources

GitHub Repository

Need Help?

Reddit Discussion

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