Which integrations are available for this server?

Supports configuration through environment variables in .env files, allowing database connection details to be securely stored and accessed. Enables querying and managing MySQL databases with support for SELECT, INSERT, UPDATE, and DELETE operations, as well as schema management through table listing, creation, description, and column addition.

How do I use MCP MySQL 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., "@MCP MySQL 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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MCP MySQL Server

by kevinbin

Overview Schema Related Servers Score Discussions

JavaScript

Remote

@enemyrr/mcp-mysql-server

A Model Context Protocol server that provides MySQL database operations. This server enables AI models to interact with MySQL databases through a standardized interface.

Installation & Setup for Cursor IDE

Installing via Smithery

To install MySQL Database Server for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @enemyrr/mcp-mysql-server --client claude

Installing Manually

Clone and build the project:

git clone https://github.com/enemyrr/mcp-mysql-server.git
cd mcp-mysql-server
npm install
npm run build

Add the server in Cursor IDE settings:
- Open Command Palette (Cmd/Ctrl + Shift + P)
- Search for "MCP: Add Server"
- Fill in the fields:
  - Name: mysql
  - Type: command
  - Command: node /absolute/path/to/mcp-mysql-server/build/index.js

Note: Replace /absolute/path/to/ with the actual path where you cloned and built the project.

Related MCP server: MCP MySQL Server

Database Configuration

You can configure the database connection in three ways:

Database URL in .env (Recommended):

DATABASE_URL=mysql://user:password@host:3306/database

Individual Parameters in .env:

DB_HOST=localhost
DB_USER=your_user
DB_PASSWORD=your_password
DB_DATABASE=your_database

Direct Connection via Tool:

use_mcp_tool({
  server_name: "mysql",
  tool_name: "connect_db",
  arguments: {
    url: "mysql://user:password@host:3306/database"
    // OR
    workspace: "/path/to/your/project" // Will use project's .env
    // OR
    host: "localhost",
    user: "your_user",
    password: "your_password",
    database: "your_database"
  }
});

Available Tools

1. connect_db

Connect to MySQL database using URL, workspace path, or direct credentials.

2. query

Execute SELECT queries with optional prepared statement parameters.

use_mcp_tool({
  server_name: "mysql",
  tool_name: "query",
  arguments: {
    sql: "SELECT * FROM users WHERE id = ?",
    params: [1]
  }
});

3. execute

Execute INSERT, UPDATE, or DELETE queries with optional prepared statement parameters.

use_mcp_tool({
  server_name: "mysql",
  tool_name: "execute",
  arguments: {
    sql: "INSERT INTO users (name, email) VALUES (?, ?)",
    params: ["John Doe", "john@example.com"]
  }
});

4. list_tables

List all tables in the connected database.

use_mcp_tool({
  server_name: "mysql",
  tool_name: "list_tables"
});

5. describe_table

Get the structure of a specific table.

use_mcp_tool({
  server_name: "mysql",
  tool_name: "describe_table",
  arguments: {
    table: "users"
  }
});

6. create_table

Create a new table with specified fields and indexes.

use_mcp_tool({
  server_name: "mysql",
  tool_name: "create_table",
  arguments: {
    table: "users",
    fields: [
      {
        name: "id",
        type: "int",
        autoIncrement: true,
        primary: true
      },
      {
        name: "email",
        type: "varchar",
        length: 255,
        nullable: false
      }
    ],
    indexes: [
      {
        name: "email_idx",
        columns: ["email"],
        unique: true
      }
    ]
  }
});

7. add_column

Add a new column to an existing table.

use_mcp_tool({
  server_name: "mysql",
  tool_name: "add_column",
  arguments: {
    table: "users",
    field: {
      name: "phone",
      type: "varchar",
      length: 20,
      nullable: true
    }
  }
});

Features

Multiple connection methods (URL, workspace, direct)
Secure connection handling with automatic cleanup
Prepared statement support for query parameters
Schema management tools
Comprehensive error handling and validation
TypeScript support
Automatic workspace detection

Security

Uses prepared statements to prevent SQL injection
Supports secure password handling through environment variables
Validates queries before execution
Automatically closes connections when done

Error Handling

The server provides detailed error messages for:

Connection failures
Invalid queries or parameters
Missing configuration
Database errors
Schema validation errors

Contributing

Contributions are welcome! Please feel free to submit a Pull Request to https://github.com/enemyrr/mcp-mysql-server

License

MIT

Available Tools

7 tools

add_columnC

Add a new column to existing table

ParametersJSON Schema

Name	Required	Description	Default
`field`	Yes
`table`	Yes

TDQS

C2.8/5.0

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 of behavioral disclosure. It states this is a mutation ('Add'), implying it modifies database structure, but doesn't cover critical aspects like permissions required, whether changes are reversible, impact on existing data, error handling, or response format. For a tool that alters schema with no annotation coverage, this is a significant gap in transparency.

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 wasted words. It's front-loaded with the core action and resource, making it easy to parse quickly. Every word ('Add', 'new column', 'existing table') contributes directly to understanding the tool's function.

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 (2 parameters with nested objects, no output schema, and no annotations), the description is incomplete. It doesn't explain the input structure (e.g., that 'field' is an object with specific properties), behavioral traits (e.g., mutation effects), or output expectations. For a schema-altering tool with rich parameters but no supporting documentation, this leaves too much undefined.

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%, meaning none of the parameters (table, field with nested properties like name, type, nullable, length, default) are documented in the schema. The description only mentions 'table' and 'column' generically, without explaining what 'field' represents or detailing the nested properties. It fails to compensate for the schema's lack of documentation, leaving key semantics unclear.

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 ('Add') and resource ('new column to existing table'), making the purpose immediately understandable. It distinguishes this tool from siblings like 'create_table' (which creates entire tables) and 'describe_table' (which inspects structure). However, it doesn't specify what kind of database or system it operates on, which could help further differentiate it from generic column-adding tools.

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. It doesn't mention prerequisites (e.g., needing an existing table from 'create_table' or 'list_tables'), exclusions (e.g., not for modifying columns), or comparisons to siblings like 'execute' (which might handle SQL directly). Without this context, an agent must infer usage from the tool name alone.

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

connect_dbC

Connect to MySQL database using URL or config

ParametersJSON Schema

Name	Required	Description
`database`	No
`host`	No
`password`	No
`url`	No	Database URL (mysql://user:pass@host:port/db)
`user`	No
`workspace`	No	Project workspace path

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 action 'connect' but doesn't describe what the connection entails (e.g., authentication requirements, rate limits, persistence, or what happens on failure). This is a significant gap for a tool that likely involves network operations and security.

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 is front-loaded with the core purpose. There is no wasted text, making it highly concise and well-structured for quick understanding.

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 a database connection tool with no annotations, no output schema, and low schema description coverage (33%), the description is incomplete. It lacks details on authentication, error handling, connection lifecycle, and how it integrates with sibling tools, making it inadequate for safe and effective use.

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 'using URL or config', which hints at the 'url' parameter and possibly others like 'host', 'user', 'password', but doesn't add detailed meaning beyond the schema. With schema description coverage at 33% (only 'url' and 'workspace' have descriptions), the description partially compensates but doesn't fully explain the semantics of all 6 parameters.

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 'connect' and resource 'MySQL database', specifying it establishes a connection. However, it doesn't differentiate from sibling tools like 'execute' or 'query' that might also involve database operations, making it clear but not sibling-distinctive.

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 'execute' or 'query', nor does it mention prerequisites such as needing to connect before performing other operations. It lacks explicit when/when-not instructions or context for usage.

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

create_tableC

Create a new table in the database

ParametersJSON Schema

Name	Required	Description
`fields`	Yes
`indexes`	No
`table`	Yes	Table name

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 'create' implies a write operation but doesn't cover critical aspects like permissions required, whether the operation is idempotent, error handling (e.g., if the table already exists), or side effects. This leaves significant gaps for a mutation 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, direct sentence with zero wasted words, making it highly efficient and front-loaded. It immediately conveys the core purpose without unnecessary elaboration, which is ideal for 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?

For a mutation tool with 3 parameters, no annotations, and no output schema, the description is insufficient. It lacks details on behavior, error cases, dependencies (e.g., connection state), and doesn't explain what 'create' entails operationally (e.g., atomicity, rollback). This leaves the agent poorly equipped to use the tool effectively in complex scenarios.

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 low at 33%, with only the 'table' parameter having a description. The tool description doesn't add any parameter details beyond the schema, failing to compensate for the coverage gap. However, the schema itself provides clear structure for 'fields' and 'indexes', so a baseline score of 3 is appropriate given the schema's clarity despite missing descriptions.

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 ('create') and resource ('new table in the database'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'add_column' or 'execute', which could also involve table creation operations, so it doesn't reach the highest 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?

The description provides no guidance on when to use this tool versus alternatives like 'execute' (which might create tables via SQL) or 'add_column' (which modifies existing tables). There's no mention of prerequisites, such as needing an established database connection via 'connect_db', leaving the agent without contextual usage cues.

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

describe_tableC

Get table structure

ParametersJSON Schema

Name	Required	Description	Default
`table`	Yes	Table name

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 carries the full burden of behavioral disclosure. 'Get table structure' implies a read-only operation, but it doesn't specify if this requires database permissions, what happens if the table doesn't exist, or the format of the returned structure. It lacks details on error handling or performance traits.

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 'Get table structure' is extremely concise and front-loaded, with no wasted words. It efficiently conveys the core purpose in three words, making it easy to scan and understand 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 a database tool with no annotations and no output schema, the description is incomplete. It doesn't explain what 'structure' includes (e.g., column definitions, indexes) or the return format, leaving gaps for an agent to understand how to use the tool effectively in context with siblings.

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 100%, with the parameter 'table' documented as 'Table name'. The description adds no additional meaning beyond this, such as examples or constraints on table names. Since the schema handles the parameter documentation adequately, the baseline score of 3 is appropriate.

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

Purpose3/5

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

The description 'Get table structure' clearly states the action (get) and resource (table structure), but it's vague about what 'structure' entails (e.g., columns, types, constraints) and doesn't distinguish it from sibling tools like 'list_tables' or 'query'. It's adequate but lacks specificity.

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. For example, it doesn't clarify if this is for metadata retrieval before operations like 'add_column' or 'query', or how it differs from 'list_tables'. The description offers no context for usage decisions.

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

executeC

Execute an INSERT, UPDATE, or DELETE query

ParametersJSON Schema

Name	Required	Description	Default
`params`	No	Query parameters (optional)
`sql`	Yes	SQL query (INSERT, UPDATE, DELETE)

TDQS

C2.9/5.0

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 of behavioral disclosure. It states the tool executes INSERT, UPDATE, or DELETE queries, implying mutation operations, but fails to disclose critical traits such as required permissions, whether changes are reversible, potential side effects, or any rate limits. This leaves significant gaps in understanding the tool's behavior.

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 extremely concise and front-loaded, consisting of a single sentence that directly states the tool's purpose without any unnecessary words. Every part of the sentence earns its place by clearly conveying the core functionality.

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 a mutation tool (executing INSERT, UPDATE, DELETE queries) with no annotations and no output schema, the description is incomplete. It lacks information on permissions, error handling, return values, or how it differs from sibling tools, making it inadequate for safe and effective use by an AI agent.

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 schema description coverage is 100%, so the input schema already documents both parameters ('sql' and 'params') adequately. The description adds minimal value by implying the 'sql' parameter should be for INSERT, UPDATE, or DELETE queries, but doesn't provide additional syntax, format details, or examples beyond what the schema specifies, meeting the baseline for high schema 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 with a specific verb ('Execute') and resource type ('INSERT, UPDATE, or DELETE query'), making it easy to understand what the tool does. However, it doesn't explicitly distinguish it from sibling tools like 'query' or 'add_column', which might also involve database operations, so it misses full sibling differentiation.

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. It doesn't mention when to choose 'execute' over sibling tools like 'query' (which might handle SELECT queries) or 'add_column' (which might be for schema modifications), leaving the agent without context for tool selection.

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

list_tablesB

List all 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 the full burden of behavioral disclosure. It states the action but doesn't mention potential behaviors like pagination, error conditions, or performance implications. For a tool with zero annotation coverage, this leaves significant gaps in understanding how it operates.

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 essential action and resource, making it highly efficient and easy to parse.

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?

Given the tool's simplicity (zero parameters, no output schema), the description is adequate as a basic overview. However, it lacks details on output format or behavioral traits, which could be important for integration. For a read-only list operation, it meets minimum viability but doesn't provide full 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 zero parameters, and the schema description coverage is 100%, so there's no need for parameter details in the description. The description appropriately focuses on the tool's purpose without redundant parameter information, aligning with the baseline for zero-parameter tools.

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 ('List') and resource ('all tables in the database'), making the purpose unambiguous. 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?

The description provides no guidance on when to use this tool versus alternatives like 'describe_table' (for table details) or 'query' (for data retrieval). It lacks context about prerequisites or typical use cases, offering minimal usage direction.

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

queryC

Execute a SELECT query

ParametersJSON Schema

Name	Required	Description	Default
`params`	No	Query parameters (optional)
`sql`	Yes	SQL SELECT query

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 full burden for behavioral disclosure. 'Execute a SELECT query' implies a read-only operation, but doesn't specify important behavioral aspects like authentication requirements, rate limits, result format, error handling, or whether it modifies database state. The description provides minimal behavioral context beyond the basic operation.

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 extremely concise at just three words, with zero wasted language. It's front-loaded with the essential information and every word earns its place. This represents optimal conciseness for a simple tool.

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?

For a database query tool with no annotations and no output schema, the description is insufficiently complete. It doesn't address critical context like return format, error conditions, security implications, or performance considerations. Given the complexity of SQL execution and the lack of structured metadata, the description should provide more operational context.

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?

With 100% schema description coverage, the schema already documents both parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema - it doesn't explain SQL syntax requirements, parameter binding conventions, or query limitations. This meets the baseline for high schema 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 'Execute a SELECT query' clearly states the action (execute) and resource (SELECT query), making the purpose immediately understandable. However, it doesn't differentiate this tool from sibling tools like 'execute' or 'describe_table' which might also involve query execution or database operations.

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. With siblings like 'execute' and 'describe_table' that likely perform related database operations, there's no indication of when this specific SELECT query tool is appropriate versus other options.

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

B3.4/5.0

Disambiguation5/5

Each tool has a clearly distinct purpose with no ambiguity: connect_db handles connections, list_tables and describe_table provide metadata, create_table and add_column manage schema, while execute and query separate write and read operations. The descriptions make it easy to distinguish between similar tools like execute vs query or create_table vs add_column.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern with clear, descriptive names (e.g., connect_db, list_tables, create_table). There are no deviations in naming conventions, and the snake_case style is applied uniformly throughout the toolset.

Tool Count5/5

With 7 tools, this server is well-scoped for basic MySQL database operations. Each tool earns its place by covering essential functions: connection management, schema operations, metadata queries, and data manipulation. The count is neither too sparse nor overwhelming for the domain.

Completeness4/5

The toolset provides solid coverage for core MySQL workflows, including connection, schema management (create, modify), and data operations (read/write). A minor gap exists in lacking explicit tools for updating or deleting tables (e.g., drop_table, alter_table), but agents can work around this using the execute tool for such DDL queries.

Maintenance

ActivityInactive

ResponsivenessNo issues

Resources

GitHub Repository

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