What can you do with this server?

The MySQL MCP Server allows interaction with MySQL databases via JSON commands in a Docker container. You can: * Execute read-only SQL queries (query) * Test SQL write queries with automatic rollback (test_execute) * List all tables in the database (list_tables) * Describe the structure of a specific table (describe_table)

Which integrations are available for this server?

Runs as a containerized service with special configurations for host connectivity, allowing MySQL database access from Docker environments with proper networking setup. Uses environment variables stored in .env files for configuration management, particularly for sensitive database connection credentials. Enables interaction with MySQL databases through read-only queries, schema exploration, and safe testing of write operations (with rollback). Provides tools for executing queries, listing tables, and describing table structures. Implemented in TypeScript, providing type-safe database interactions with MySQL.

How do I use MySQL MCP Server?

1. Click on "Install 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., "@MySQL MCP Server list all tables in the database" 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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MySQL MCP Server

by xiangma9712

Overview Schema Related Servers Score Discussions

JavaScript

Remote

MySQL MCP Server

An MCP server for interacting with MySQL databases.

This server supports executing read-only queries (query) and write queries that are ultimately rolled back (test_execute).

Setup

Environment Variables

Add the following environment variables to ~/.mcp/.env:

MYSQL_HOST=host.docker.internal  # Hostname to access host services from Docker container
MYSQL_PORT=3306
MYSQL_USER=root
MYSQL_PASSWORD=your_password

Note: host.docker.internal is a special DNS name for accessing host machine services from Docker containers. Use this setting when connecting to a MySQL server running on your host machine. If connecting to a different MySQL server, change to the appropriate hostname.

mcp.json Configuration

{
  "mcpServers": {
    "mysql": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "--add-host=host.docker.internal:host-gateway",
        "--env-file",
        "/Users/username/.mcp/.env",
        "ghcr.io/xiangma9712/mcp/mysql"
      ]
    }
  }
}

Related MCP server: MCP MySQL Server

Usage

Starting the Server

docker run -i --rm --add-host=host.docker.internal:host-gateway --env-file ~/.mcp/.env ghcr.io/xiangma9712/mcp/mysql

Note: If you're using OrbStack, host.docker.internal is automatically supported, so the --add-host option can be omitted. While Docker Desktop also typically supports this automatically, adding the --add-host option is recommended for better reliability.

Available Commands

1. Execute Read-only Query

{
  "type": "query",
  "payload": {
    "sql": "SELECT * FROM your_table"
  }
}

Response:

{
  "success": true,
  "data": [
    {
      "id": 1,
      "name": "example"
    }
  ]
}

2. Test Query Execution

{
  "type": "test_execute",
  "payload": {
    "sql": "UPDATE your_table SET name = 'updated' WHERE id = 1"
  }
}

Response:

{
  "success": true,
  "data": "The UPDATE SQL query can be executed."
}

3. List Tables

{
  "type": "list_tables"
}

Response:

{
  "success": true,
  "data": ["table1", "table2", "table3"]
}

4. Describe Table

{
  "type": "describe_table",
  "payload": {
    "table": "your_table"
  }
}

Response:

{
  "success": true,
  "data": [
    {
      "Field": "id",
      "Type": "int(11)",
      "Null": "NO",
      "Key": "PRI",
      "Default": null,
      "Extra": ""
    },
    {
      "Field": "name",
      "Type": "varchar(255)",
      "Null": "YES",
      "Key": "",
      "Default": null,
      "Extra": ""
    }
  ]
}

Implementation Details

Implemented in TypeScript
Uses mysql2 package
Runs as a Docker container
Accepts JSON commands through standard input
Returns JSON responses through standard output
Uses host.docker.internal to connect to host MySQL (compatible with both OrbStack and Docker Desktop)

Security Considerations

Uses environment variables for sensitive information management
SQL injection prevention is the implementer's responsibility
Proper network configuration required for production use
Appropriate firewall settings needed when connecting to host machine services

Available Tools

4 tools

describe_tableC

Retrieves column information for a table.

ParametersJSON Schema

Name	Required	Description	Default
`tableName`	Yes

TDQS

C2.9/5.0

Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool retrieves information, implying a read-only operation, but doesn't specify permissions required, rate limits, error handling, or the format of returned column information, leaving significant gaps for a tool with no structured safety hints.

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

Conciseness5/5

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

The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded and wastes no space, making it easy for an agent to parse quickly.

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 annotations, no output schema, and low schema coverage, the description is incomplete. It lacks details on behavioral traits, parameter constraints, and return values, which are critical for a tool that retrieves structured data. This leaves the agent with insufficient context for reliable invocation.

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?

The description mentions 'tableName' implicitly by referring to 'a table', but with 0% schema description coverage and one parameter, it adds minimal semantic value beyond what the schema's property name suggests. Since schema coverage is low, the description doesn't compensate with details like valid table name formats or examples, meeting the baseline for a single parameter.

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 the action ('Retrieves') and resource ('column information for a table'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from siblings like 'list_tables' (which likely lists table names) or 'query' (which might execute SQL queries), missing full sibling distinction.

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 provides no guidance on when to use this tool versus alternatives like 'list_tables' or 'query'. It lacks context about prerequisites, such as needing an existing table name, or exclusions, leaving the agent to infer usage from the purpose alone.

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

list_tablesB

Retrieves a list of tables in the database.

ParametersJSON Schema

Name	Required	Description	Default
No parameters

TDQS

B3.2/5.0

Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states it 'retrieves' data (implying read-only), but doesn't mention any constraints like permissions needed, rate limits, pagination, or what format the list returns. This leaves significant behavioral gaps for a tool with zero annotation coverage.

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

Conciseness5/5

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

The description is a single, clear sentence with no wasted words. It's front-loaded with the core purpose and efficiently communicates the essential function without redundancy or fluff.

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

Completeness3/5

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

For a simple list-retrieval tool with no parameters and no output schema, the description is minimally adequate. However, without annotations or output details, it lacks completeness regarding behavioral aspects like return format or constraints. It meets basic needs but has clear gaps in context.

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?

The tool has 0 parameters, and schema description coverage is 100% (though trivial since there's no schema). The description doesn't need to explain parameters, so it appropriately avoids unnecessary detail. A baseline of 4 is justified as it doesn't mislead about parameters that don't exist.

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 the action ('Retrieves') and resource ('list of tables in the database'), making the tool's purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'describe_table' or 'query', which prevents a perfect score.

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 like 'describe_table' (for table details) or 'query' (for data retrieval). The description only states what it does, not when it's appropriate, leaving the agent to infer usage context.

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

queryB

Executes a read-only SQL query.

ParametersJSON Schema

Name	Required	Description	Default
`sql`	Yes

TDQS

B3/5.0

Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses 'read-only', indicating non-destructive behavior, which is useful. However, it lacks details on permissions, rate limits, error handling, or response format, leaving significant behavioral gaps for a tool that executes SQL queries.

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

Conciseness5/5

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

The description is a single, efficient sentence with zero waste. It's appropriately sized and front-loaded, clearly stating the core action without unnecessary elaboration, making it easy for an agent to parse quickly.

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 complexity of executing SQL queries, no annotations, no output schema, and low schema coverage, the description is incomplete. It lacks information on return values, error cases, or operational context, making it inadequate for safe and effective tool invocation.

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 description coverage is 0%, so the schema provides no parameter details. The description adds minimal semantics by implying the 'sql' parameter is for SQL queries, but it doesn't explain syntax, supported SQL dialects, or constraints, failing to compensate for the low coverage.

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 the verb ('executes') and resource ('SQL query'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'test_execute' or 'describe_table', which might also involve query execution or database operations, so it's not fully specific to sibling context.

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 provides no guidance on when to use this tool versus alternatives like 'test_execute' or 'list_tables'. It mentions 'read-only', which hints at usage context, but lacks explicit when/when-not instructions or named alternatives, leaving the agent with minimal direction.

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

test_executeB

Checks if an SQL query can be executed and rolls back afterward.

ParametersJSON Schema

Name	Required	Description	Default
`sql`	Yes

TDQS

B3/5.0

Behavior2/5

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

No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: the tool checks executability and performs a rollback, indicating it's a safe, non-destructive operation. However, it doesn't mention error handling, performance implications, or what 'checks' entails (e.g., syntax validation, permission checks), leaving gaps for a mutation-related tool.

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

Conciseness5/5

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

The description is a single, efficient sentence: 'Checks if an SQL query can be executed and rolls back afterward.' It's front-loaded with the core purpose, has zero waste, and appropriately sized for the tool's complexity.

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 involves SQL execution testing (a mutation-related operation), no annotations, no output schema, and low parameter coverage, the description is incomplete. It lacks details on return values (e.g., success/failure indicators), error responses, or rollback specifics, making it inadequate for safe agent use.

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 description coverage is 0%, so the description must compensate. It mentions 'SQL query' as the input, which adds meaning to the 'sql' parameter by specifying it's an SQL query string. However, it doesn't provide details on query format, supported SQL dialects, or constraints, failing to fully compensate for the low coverage.

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 the tool's purpose: 'Checks if an SQL query can be executed and rolls back afterward.' This specifies the verb ('checks') and resource ('SQL query'), and distinguishes it from siblings like 'query' (which likely executes queries) and 'describe_table'/'list_tables' (which are read-only metadata tools). However, it doesn't explicitly differentiate from 'query' beyond implying a test vs. execution distinction, keeping it at 4 rather than 5.

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

Usage Guidelines3/5

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

The description implies usage context: it's for testing SQL queries before actual execution, suggesting when to use it (to validate queries) versus alternatives like 'query' (for actual execution). However, it doesn't explicitly state when-not-to-use or name alternatives, and lacks prerequisites (e.g., database connection requirements), so it's not fully explicit.

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

Install Server

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TDQS

B3.3/5.0

Disambiguation5/5

Each tool has a clearly distinct purpose: describe_table for column metadata, list_tables for table enumeration, query for read-only SQL execution, and test_execute for query validation with rollback. There is no overlap in functionality, making tool selection straightforward for an agent.

Naming Consistency4/5

The naming follows a consistent verb_noun pattern (describe_table, list_tables, query, test_execute), with 'query' as a minor deviation as it lacks a noun suffix. Overall, the pattern is predictable and readable, though not perfectly uniform.

Tool Count4/5

With 4 tools, the count is reasonable for a database server, covering essential operations like listing tables, describing schema, querying, and testing queries. It is slightly lean but well-scoped, lacking only advanced features like write operations or transaction management.

Completeness3/5

The toolset covers basic read and validation operations but has notable gaps: there are no tools for data manipulation (e.g., insert, update, delete), schema modification (e.g., create_table), or transaction control. This limits the server to read-only and diagnostic tasks, which may cause agent failures for write workflows.

Maintenance

ActivityInactive

ResponsivenessSyncing

Resources

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