MySql MCP Server
The MySQL MCP Server bridges AI models and MySQL databases through a structured interface following the Model Context Protocol, enabling AI assistants to interact with MySQL data. With this server, you can:
Connect to MySQL databases using specified credentials
Execute SQL queries and handle prepared statements
Inspect database structure (list tables, describe table structures)
List and switch between accessible databases
Monitor and check connection status
Toggle read-only mode for database access
Disconnect from the current database
Test tools via web interface
Deploy with Docker
Monitor in real-time using Server-Sent Events (SSE)
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MySql MCP Servershow me the top 10 customers by total purchase amount"
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.
MySQL MCP Server
A Model Context Protocol (MCP) server for MySQL databases that enables AI models to interact with MySQL databases through a structured interface.
Overview
The MySQL MCP Server provides a bridge between AI models and MySQL databases, allowing AI agents to query and analyze MySQL data. This implementation follows the Model Context Protocol specification and offers both web server and CLI modes of operation.
Related MCP server: MySQL MCP Server
Features
MySQL database connection management
SQL query execution
Table listing and structure inspection
Database listing and selection
Real-time status monitoring via SSE (Server-Sent Events)
Web interface for testing MCP tools
Support for both stdio and SSE transport methods
Docker deployment ready
Installation
# Global installation
npm install -g mysql-mcp
# Local installation
npm install mysql-mcpUsing with AI Assistants
Using the Published Server on Smithery.ai
The MySQL MCP Server is published on Smithery.ai and can be easily used with various AI assistants:
Access the server: Visit https://smithery.ai/server/@sussa3007/mysql-mcp
Configure the server:
Set your MySQL database connection details:
MYSQL_HOST
MYSQL_PORT
MYSQL_USER
MYSQL_PASSWORD
MYSQL_DATABASE
MYSQL_READONLY (optional, set to True for read-only access)
Connect with supported AI platforms:
Anthropic Claude
Cursor AI
Windsurf
Cline
Witsy
Enconvo
Goose
Authentication: Login to Smithery.ai to save your configuration and generate authentication tokens.
Use in AI prompts: Once connected, you can utilize MySQL tools in your AI conversations by asking the assistant to perform database operations.
Using After Local Installation
To use a locally developed version:
Run
npm linkin your project directoryConfigure your settings file as follows:
{
"mcpServers": {
"mysql": {
"command": "node",
"args": ["mysql-mcp"],
"env": {
"MYSQL_HOST": "localhost",
"MYSQL_PORT": "3306",
"MYSQL_USER": "root",
"MYSQL_PASSWORD": "your_password",
"MYSQL_DATABASE": "your_database",
"MYSQL_READONLY": "true"
}
}
}
}Tools
status
Check the current database connection status.
Inputs: No parameters required
Returns: Connection status information, including host, port, database, and username if connected.
connect
Connect to a MySQL database.
Inputs:
host (optional string): Database server hostname or IP address
port (optional string): Database server port
user (optional string): Database username
password (optional string): Database password
database (optional string): Database name to connect to
Returns: Connection success message or error details.
disconnect
Close the current MySQL database connection.
Inputs: No parameters required
Returns: Disconnection success message or error details.
query
Execute an SQL query on the connected database.
Inputs:
sql (string): SQL query to execute
params (optional array): Parameters for prepared statements
Returns: Query results as JSON or error message.
list_tables
Get a list of tables in the current database.
Inputs: No parameters required
Returns: List of table names in the current database.
describe_table
Get the structure of a specific table.
Inputs:
table (string): Name of the table to describe
Returns: Table structure details including columns, types, keys, and other attributes.
list_databases
Get a list of all accessible databases on the server.
Inputs: No parameters required
Returns: List of database names available on the server.
use_database
Switch to a different database.
Inputs:
database (string): Name of the database to switch to
Returns: Confirmation message or error details.
Keywords
mysql, mcp, database, ai, model context protocol
License
MIT
Available Tools
9 toolsconnectC
Connect to a MySQL database.
| Name | Required | Description | Default |
|---|---|---|---|
| database | No | Database name to connect to | |
| host | No | Database server hostname or IP address | |
| password | No | Database password | |
| port | No | Database server port | |
| user | No | Database username |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description lacks behavioral details beyond the action. It does not disclose side effects (e.g., establishing a persistent connection), required permissions, or what happens if already connected. With no annotations, the description carries the full burden but provides minimal 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, front-loaded sentence with no wasted words. However, for a tool with 5 parameters and 8 sibling tools, some additional context could be beneficial, preventing it from being perfect.
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 absence of an output schema and annotations, the description should cover return values, error handling, and behavioral context. It does not explain what the tool returns (e.g., connection success), prerequisites, or whether multiple connections are supported, making it incomplete.
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 input schema has 100% coverage, describing each parameter (host, port, user, password, database) adequately. The description adds no additional semantic value beyond what the schema already provides, so baseline score of 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 clearly states the tool connects to a MySQL database, using a specific verb+resource. However, it does not distinguish from sibling tools like 'use_database' or 'status', which could cause confusion.
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 such as 'use_database' or 'status'. There are no usage contexts, prerequisites, or exclusions mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
describe_tableA
Get the structure of a specific table.
| Name | Required | Description | Default |
|---|---|---|---|
| table | Yes | Name of the table to describe |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It does not disclose any behavioral traits such as safety, permissions, or potential effects. A read operation like 'get' could be assumed safe, but it's not stated.
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?
Single sentence, no wasted words. Front-loaded with clear action and resource.
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 simple tool with one parameter and no output schema, the description is complete enough. It could optionally mention that it returns column names and types, but the structure of a table implies that.
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% (the only parameter 'table' has a description in the schema). The tool description adds no additional meaning beyond what the schema already provides. 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 the structure of a specific table' uses a specific verb ('get') and clearly identifies the resource ('structure of a specific table'). It distinguishes this tool from siblings like 'list_tables' (which lists table names) and 'query' (which runs SQL).
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 explicit guidance on when to use or not use this tool. The description implies using it when you need table schema, but does not mention alternatives or when to avoid it. It's minimally adequate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
disconnectB
Close the current MySQL database connection.
| Name | Required | Description | Default |
|---|---|---|---|
| random_string | Yes | Dummy parameter for no-parameter tools |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It only states the action without detailing side effects, error states (e.g., if already disconnected), or transaction handling.
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?
Single sentence, no wasted words. However, could include additional context without being verbose.
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 tool simplicity, description covers the basic action but omits prerequisites (e.g., must be connected) and return behavior. Adequate but not comprehensive.
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 a single required dummy parameter described in schema. Description adds no meaning beyond the schema, failing to explain why the parameter exists.
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 'Close the current MySQL database connection' uses a specific verb ('Close') and resource ('current MySQL database connection'), clearly distinguishing it from sibling tools like 'connect'.
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 explicit guidance on when to use this tool versus alternatives. While the purpose is obvious (after queries are done), the description lacks when-not or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_databasesB
Get a list of all accessible databases on the server.
| Name | Required | Description | Default |
|---|---|---|---|
| random_string | Yes | Dummy parameter for no-parameter tools |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden for behavioral transparency, but it only states it lists accessible databases. It does not disclose whether it requires a connection, has side effects, or note that the 'random_string' parameter is a dummy.
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, clear sentence with no unnecessary information. It is front-loaded and concise.
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 simple list operation, the description is mostly complete. It omits details about the return format or pagination, but these may be inferred. Given no output schema, a bit more detail would be beneficial, but it is adequate.
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% (the sole parameter 'random_string' is described as a dummy). The tool description adds no additional meaning for the parameter, which is acceptable given its dummy nature, but does not enrich understanding beyond the schema.
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's purpose: 'Get a list of all accessible databases on the server.' It uses a specific verb ('Get') and resource ('list of ... databases'), and distinguishes itself from siblings like 'list_tables' and 'use_database'.
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 or when not to use this tool. It does not mention prerequisites such as needing an active connection or any context for its use compared to alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_tablesB
Get a list of tables in the current database.
| Name | Required | Description | Default |
|---|---|---|---|
| random_string | Yes | Dummy parameter for no-parameter tools |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description bears full responsibility. It fails to disclose the dummy parameter requirement, return format, or safety profile, leaving behavioral gaps.
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 very concise with one short sentence. While efficient, it omits return type info and dummy parameter mention, but for a simple list tool it's acceptable.
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?
Without output schema, the description should explain return values (e.g., array of table names). It also fails to clarify the required dummy parameter, making it incomplete for an agent.
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% because the dummy parameter's description explains its purpose. The tool description adds no parameter details, meeting the baseline for high coverage.
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 verb 'Get' and resource 'list of tables' with context 'current database', distinguishing it from siblings like list_databases and describe_table.
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 implies using this tool to browse tables but provides no explicit when-to-use or when-not-to-use guidance compared to siblings. It lacks exclusions or context about prerequisites like setting a database first.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
queryC
Execute an SQL query on the connected database.
| Name | Required | Description | Default |
|---|---|---|---|
| params | No | Parameters for prepared statements | |
| sql | Yes | SQL query to execute |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, and the description only states 'execute an SQL query'. It does not disclose whether queries are read-only, side effects, error handling, 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence, front-loading the essential action with no unnecessary words.
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 lack of annotations and output schema, the description is incomplete. It misses details about return value format, safety, or limitations for a database query tool.
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% and parameters are described within the schema. The description adds no additional meaning beyond what the schema already provides.
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 executes SQL queries, which is specific and aligns with the name. However, it does not differentiate from sibling tools like 'describe_table' or 'list_tables', but the purpose is unambiguous.
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 on when to use this tool versus alternatives like 'describe_table' or 'status'. Missing context on prerequisites or typical scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
set_readonlyB
Enable or disable read-only mode
| Name | Required | Description | Default |
|---|---|---|---|
| readonly | Yes | Set to true to enable read-only mode, false to disable |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must fully disclose behavior, but it only states the basic action. It does not explain consequences of enabling read-only mode, scope, or reversibility, which is insufficient for safe selection.
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 with no filler. It is appropriately front-loaded, though it could optionally expand on context without harming conciseness.
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 low complexity (1 boolean param, no output schema), the description covers the basic action but lacks context about the tool's effect on the system, such as whether it affects a session or database, 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 a clear parameter description. The tool description adds minimal value beyond restating the parameter purpose, so it meets the baseline without improvement.
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 (enable/disable) and the resource (read-only mode). It is distinct from sibling tools which focus on database operations like querying or connecting.
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 vs alternatives, such as query or status. There are no context clues or exclusions, leaving the agent without decision support.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
statusC
Check the current database connection status.
| Name | Required | Description | Default |
|---|---|---|---|
| random_string | Yes | Dummy parameter for no-parameter tools |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries full burden. It implies a read operation but does not disclose side effects, authentication needs, rate limits, or return format. The phrase 'check' is vague.
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 sentence with 6 words, highly concise and front-loaded. No extraneous 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?
For a simple status check, the description is minimally adequate. However, it lacks information about the return value (e.g., format, fields) and any potential side effects, making it slightly incomplete.
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 description does not address the single parameter 'random_string', which is a dummy required parameter. The schema already describes it, but the description misses an opportunity to clarify that it can be ignored.
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 checks the current database connection status, using a specific verb and resource. It is distinguishable from siblings like connect and disconnect. However, it could be more precise about what 'status' entails.
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 (e.g., connect for establishing a connection). There is no mention of prerequisites or context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
use_databaseB
Switch to a different database.
| Name | Required | Description | Default |
|---|---|---|---|
| database | Yes | Name of the database to switch to |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It states an action but does not disclose side effects (e.g., impact on subsequent queries, reversibility, validation of database names). Minimal transparency beyond the stated purpose.
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 sentence, concise and front-loaded. It communicates the core action without wasted words.
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 simple tool with one parameter and no output schema, the description covers the basic purpose. However, it lacks behavioral details (e.g., what 'switch' means for state or session) which would be beneficial for completeness.
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%, so the parameter description already exists. The tool description adds no extra meaning beyond the schema's 'Name of the database to switch to'. Baseline 3 is appropriate as no additional semantics are provided.
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 'Switch to a different database' clearly states the verb (switch) and resource (database). It distinguishes the tool from siblings like 'connect' (establish connection) and 'list_databases' (list databases), making its unique purpose obvious.
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 does not mention prerequisites (e.g., a current connection) or contrast with 'connect' or 'set_readonly'. The implicit usage is clear, but explicit context is missing.
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.
9 tool updates
v1.0.0- First observed
connect - First observed
describe_table - First observed
disconnect - First observed
list_databases - First observed
list_tables - First observed
query - First observed
set_readonly - First observed
status - First observed
use_database
TDQS
Scored across 9 tools
Each tool has a clearly distinct purpose with no ambiguity: connection management (connect, disconnect, status), database navigation (list_databases, use_database), table operations (list_tables, describe_table), query execution (query), and mode control (set_readonly). The descriptions reinforce non-overlapping functionality.
All tool names follow a consistent snake_case pattern with clear verb_noun combinations (e.g., list_databases, describe_table, use_database). The naming is predictable and readable throughout the set.
With 9 tools, the count is well-scoped for a MySQL database server, covering essential operations like connection management, database/table listing, query execution, and configuration. Each tool earns its place without redundancy.
The tool set provides strong coverage for core MySQL operations, including CRUD-like functionality via the query tool and lifecycle management. A minor gap is the lack of explicit tools for schema modification (e.g., create/alter/drop tables), but agents can work around this using SQL queries.
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