Un servidor de Protocolo de Contexto de Modelo que proporciona operaciones con bases de datos MySQL. Este servidor permite que los modelos de IA interactúen con bases de datos MySQL mediante una interfaz estandarizada.
Para instalar MySQL Database Server para Claude Desktop automáticamente a través de Smithery :
npx -y @smithery/cli install @enemyrr/mcp-mysql-server --client claudeClonar y construir el proyecto:
git clone https://github.com/enemyrr/mcp-mysql-server.git
cd mcp-mysql-server
npm install
npm run buildAgregue el servidor en la configuración de Cursor IDE:
Abrir la paleta de comandos (Cmd/Ctrl + Shift + P)
Buscar "MCP: Agregar servidor"
Rellene los campos:
Nombre: mysql
Tipo: command
Comando: node /absolute/path/to/mcp-mysql-server/build/index.js
Nota : Reemplace
/absolute/path/to/con la ruta real donde clonó y construyó el proyecto.
Related MCP server: MySQL MCP Server
Puede configurar la conexión a la base de datos de tres maneras:
URL de la base de datos en .env (recomendado):
DATABASE_URL=mysql://user:password@host:3306/databaseParámetros individuales en .env :
DB_HOST=localhost
DB_USER=your_user
DB_PASSWORD=your_password
DB_DATABASE=your_databaseConexión directa mediante herramienta :
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"
}
});Conéctese a la base de datos MySQL mediante URL, ruta del espacio de trabajo o credenciales directas.
Ejecutar consultas SELECT con parámetros de declaración preparada opcionales.
use_mcp_tool({
server_name: "mysql",
tool_name: "query",
arguments: {
sql: "SELECT * FROM users WHERE id = ?",
params: [1]
}
});Ejecute consultas INSERT, UPDATE o DELETE con parámetros de declaración preparada opcionales.
use_mcp_tool({
server_name: "mysql",
tool_name: "execute",
arguments: {
sql: "INSERT INTO users (name, email) VALUES (?, ?)",
params: ["John Doe", "john@example.com"]
}
});Enumere todas las tablas en la base de datos conectada.
use_mcp_tool({
server_name: "mysql",
tool_name: "list_tables"
});Obtener la estructura de una tabla específica.
use_mcp_tool({
server_name: "mysql",
tool_name: "describe_table",
arguments: {
table: "users"
}
});Crea una nueva tabla con campos e índices específicos.
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
}
]
}
});Agregar una nueva columna a una tabla existente.
use_mcp_tool({
server_name: "mysql",
tool_name: "add_column",
arguments: {
table: "users",
field: {
name: "phone",
type: "varchar",
length: 20,
nullable: true
}
}
});Múltiples métodos de conexión (URL, espacio de trabajo, directo)
Manejo seguro de conexiones con limpieza automática
Soporte de declaraciones preparadas para parámetros de consulta
Herramientas de gestión de esquemas
Manejo integral de errores y validación
Compatibilidad con TypeScript
Detección automática del espacio de trabajo
Utiliza sentencias preparadas para evitar la inyección de SQL
Admite el manejo seguro de contraseñas a través de variables de entorno
Valida las consultas antes de su ejecución
Cierra automáticamente las conexiones cuando termina
El servidor proporciona mensajes de error detallados para:
Fallos de conexión
Consultas o parámetros no válidos
Configuración faltante
Errores de base de datos
Errores de validación del esquema
¡Agradecemos sus contribuciones! No dude en enviar una solicitud de incorporación de cambios a https://github.com/enemyrr/mcp-mysql-server
Instituto Tecnológico de Massachusetts (MIT)
Add a new column to existing table
| Name | Required | Description | Default |
|---|---|---|---|
| field | Yes | ||
| table | Yes |
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 this is a mutation operation ('Add'), implying it modifies the database schema, but doesn't mention permissions required, whether it's reversible, potential side effects on existing data, or error conditions. This leaves significant gaps for a tool that alters database structure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that gets straight to the point with no wasted words. It's appropriately sized for a simple tool and front-loads the core action without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (database schema mutation with nested parameters), lack of annotations, and no output schema, the description is inadequate. It doesn't cover behavioral aspects like permissions or side effects, parameter details, or what to expect upon success/failure. For a tool that modifies database structure, this leaves too many unknowns for safe and effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 but fails to do so. It doesn't explain what 'table' and 'field' parameters represent, their expected formats, or the nested structure of 'field' with properties like 'name', 'type', 'nullable', etc. The description adds no meaningful parameter context beyond what's minimally implied by the tool name.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Add') and the target resource ('new column to existing table'), making the purpose immediately understandable. It doesn't distinguish from siblings like 'create_table' or 'describe_table', but it's specific enough to avoid confusion with other database operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 'create_table' for new tables or 'execute' for general SQL commands. There's no mention of prerequisites, such as needing an existing table, or any context about when this operation is appropriate versus other schema modifications.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Connect to MySQL database using URL or config
| Name | Required | Description | Default |
|---|---|---|---|
| database | No | ||
| host | No | ||
| password | No | ||
| url | No | Database URL (mysql://user:pass@host:port/db) | |
| user | No | ||
| workspace | No | Project workspace path |
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 connects to a database, implying it establishes a session or handle, but doesn't describe what happens after connection (e.g., persistence, timeout, error handling), authentication needs beyond parameters, or side effects. For a tool with zero 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise—a single sentence that directly states the tool's purpose and method without any fluff. It's front-loaded and wastes no words, making it easy for an agent to parse quickly. Every part of the sentence earns its place by conveying essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (database connection with 6 parameters), lack of annotations, no output schema, and low schema description coverage, the description is incomplete. It doesn't cover behavioral aspects like connection lifecycle, error cases, or output format, leaving critical gaps for the agent to infer. This is inadequate for a tool that likely has significant operational implications.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 33% (only 'url' and 'workspace' have descriptions), so the description must compensate but adds minimal value. It mentions 'URL or config', hinting at parameters like 'url' and possibly 'host', 'user', etc., but doesn't explain their semantics, relationships (e.g., mutual exclusivity), or defaults. The description provides some context but doesn't fully bridge the coverage gap, warranting a baseline score.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Connect to MySQL database') and the resource ('MySQL database'), making the purpose evident. It specifies the connection method ('using URL or config'), which helps distinguish it from other database operations. However, it doesn't explicitly differentiate from sibling tools like 'execute' or 'query', which might also involve database interactions, so it's not a perfect 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 this connection before using other tools like 'query'), exclusions, or contextual cues. With sibling tools like 'execute' and 'query' available, the lack of usage guidelines leaves the agent uncertain about proper sequencing or selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Create a new table in the database
| Name | Required | Description | Default |
|---|---|---|---|
| fields | Yes | ||
| indexes | No | ||
| table | Yes | Table name |
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' which implies a write/mutation operation, but doesn't cover critical aspects like required permissions, whether the operation is idempotent, error handling, or what happens on conflicts (e.g., if the table already exists). This leaves significant gaps for a tool that modifies database state.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
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.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 3 parameters, low schema description coverage (33%), no annotations, and no output schema, the description is inadequate. It doesn't compensate for the missing behavioral context (e.g., mutation effects, error cases) or parameter details, leaving the agent poorly equipped to use this tool correctly in a database environment.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 documented. The description adds no parameter semantics beyond what the schema provides—it doesn't explain the purpose of 'fields' or 'indexes', their structure, or constraints. However, the schema itself defines these parameters with detailed properties, providing some baseline understanding despite the coverage gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
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'), providing a specific verb+resource combination. However, it doesn't differentiate from sibling tools like 'add_column' or 'execute', which might also involve database modifications, leaving room for ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 mention prerequisites like needing an existing database connection (which 'connect_db' might handle) or when to use 'execute' for other SQL operations, 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.
Get table structure
| Name | Required | Description | Default |
|---|---|---|---|
| table | Yes | Table name |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description fails to disclose behavioral traits like read-only nature, side effects, or required permissions. It simply states the function without additional context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise (two words), front-loaded, and gets straight to the point. However, it could benefit from a bit more detail without sacrificing brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool is simple with one parameter and no output schema, the description is minimal but adequate for a basic understanding. However, it doesn't specify what 'structure' includes (e.g., columns, types), leaving some ambiguity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with the only parameter 'table' described as 'Table name'. The description adds no new meaning beyond the schema, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Get table structure' clearly states the verb (Get) and resource (table structure), and distinguishes the tool from siblings like execute, list_tables, and query, which have different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 vs alternatives, such as list_tables or query. It lacks context on when not to use it or any prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Execute an INSERT, UPDATE, or DELETE query
| Name | Required | Description | Default |
|---|---|---|---|
| params | No | An optional array of parameters (as strings) to bind to the SQL query placeholders (e.g., ?). | |
| sql | Yes | The SQL query string (INSERT, UPDATE, DELETE) to execute. |
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 executes SQL queries but fails to mention critical traits like authentication requirements, transaction handling, error behavior, or potential side effects (e.g., data modification risks). 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise and front-loaded, consisting of a single, direct sentence that 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.
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 (a potentially destructive operation), the lack of annotations, and no output schema, the description is incomplete. It does not address safety considerations, return values, or error handling, making it inadequate for informed tool selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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') thoroughly. The description adds no additional semantic meaning beyond what the schema provides, such as examples or constraints, resulting in a baseline score of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Execute') and the type of operations ('INSERT, UPDATE, or DELETE query'), making the purpose specific and understandable. However, it does not explicitly distinguish this tool from sibling tools like 'query' (which might handle SELECT queries), leaving room for ambiguity in differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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, such as 'query' for SELECT operations or other siblings for schema modifications. It lacks explicit instructions on prerequisites, context, or exclusions, 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.
List all tables in the database
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description must fully disclose behavior. It merely states 'List all tables', which implies a read operation, but omits details on permissions, output format, or performance implications.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with no unnecessary words. It is appropriately front-loaded but lacks any structural enhancement like bullet points that could aid readability.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema or annotations, the description should explain what 'list' returns (e.g., table names, schemas). Without this, an agent may not know how to use the result. Sibling tools like 'describe_table' suggest additional context might be needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has no parameters, so the description cannot add parameter meaning. Baseline for zero parameters is 4, and the description is sufficient for the parameterless case.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists all tables in the database. While it distinguishes from 'describe_table' by listing instead of describing, it does not explicitly differentiate from potential siblings like 'query' or 'execute'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 its siblings (describe_table, execute, query). It is implied that it retrieves table names, but no context on prerequisites or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Execute a SELECT query
| Name | Required | Description | Default |
|---|---|---|---|
| params | No | An optional array of parameters (as strings) to bind to the SQL query placeholders (e.g., ?). | |
| sql | Yes | The SQL SELECT query string to execute. |
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. 'Execute a SELECT query' implies a read-only operation, but it doesn't specify critical behaviors: whether it requires an active connection, handles errors, returns results in a specific format, has rate limits, or affects database state. For a tool with zero 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste—'Execute a SELECT query' is front-loaded and precisely conveys the core action. Every word earns its place, making it highly concise and well-structured for quick comprehension.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of SQL execution (requires connection, error handling, result formatting) and the lack of annotations and output schema, the description is incomplete. It doesn't address prerequisites, behavioral traits, or return values, leaving the agent with insufficient context to use the tool effectively beyond basic parameter passing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents both parameters ('sql' and 'params'). The description adds no additional meaning beyond what's in the schema—it doesn't explain parameter interactions, syntax examples, or constraints. Baseline 3 is appropriate when the schema does all the work.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
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 verb ('Execute') and resource ('SELECT query'), making the purpose unambiguous. It distinguishes from siblings like 'execute' (which might handle other SQL types) by specifying SELECT queries only. However, it doesn't explicitly differentiate from all siblings (e.g., 'describe_table' also reads data), so it's not a perfect 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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', 'list_tables', or 'describe_table'. It doesn't mention prerequisites (e.g., database connection), exclusions (e.g., non-SELECT queries), or contextual cues. This leaves the agent to infer usage from the name and schema alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.0add_columnconnect_dbcreate_tabledescribe_tableexecutelist_tablesqueryScored across 7 tools
Each tool has a clearly distinct purpose with no ambiguity. For example, 'execute' handles INSERT/UPDATE/DELETE while 'query' handles SELECT, and 'describe_table' provides structure while 'list_tables' enumerates tables. The separation between schema operations (add_column, create_table) and data operations (execute, query) is well-defined.
All tools follow a consistent verb_noun pattern with clear, descriptive names. The naming convention is uniform throughout (e.g., add_column, create_table, describe_table, list_tables), making it easy to predict functionality. There are no deviations in style or formatting.
With 7 tools, this server is well-scoped for MySQL database operations. Each tool serves a specific, necessary function in the database management workflow, from connection (connect_db) to schema manipulation (create_table, add_column) and data querying (query, execute). The count avoids both bloat and insufficiency.
The tool set covers core MySQL operations effectively, including connection, schema management, and data querying. Minor gaps exist, such as the lack of tools for updating or deleting tables, which could limit full lifecycle management. However, agents can work around this using existing tools like 'execute' for DROP TABLE queries.
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