The MariaDB Reader MCP Server allows AI assistants to explore and interact with MariaDB databases through the following capabilities:
List Databases: Retrieve a list of all accessible databases
List Tables: Obtain a list of all tables within a specified database
Get Table Schema: Fetch the schema (column definitions) of a specified table
Query Table: Query data from a specified table, with an optional limit on the number of rows returned (defaults to 100)
Enables database exploration and interaction with MariaDB databases, providing tools to list databases, view tables, retrieve table schemas, and query table data with customizable limits.
Click on "Install 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., "@MariaDB Reader MCP Server show me the schema for the users table in the ecommerce 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.
This project is a Model Context Protocol (MCP) server for exploring and interacting with MariaDB databases. It provides tools for AI assistants like Cline to access and query MariaDB databases.
This MCP server provides the following tools:
list_databases : Returns a list of all accessible databases.
list_tables : Returns a list of all tables in the specified database.
Input: database (string, required) - The name of the database for which to retrieve a list of tables.
get_table_schema : Returns the schema (column definition) of the specified table.
input:
database (string, required) - The name of the database to which the table belongs.
table (string, required) - The name of the table to query the schema for.
query_table : Retrieves data from a specified table. By default, it returns the first 100 rows.
input:
database (string, required) - The name of the database to which the table belongs.
table (string, required) - The name of the table from which to retrieve data.
limit (number, optional) - The maximum number of rows to return (default: 100).
Related MCP server: MCP MariaDB Server
To use this server, you need to clone the GitHub repository and register the server information in the configuration file of your MCP client (e.g. the VS Code extension). This repository includes a pre-built executable ( build/index.js ), so no separate build step is required.
Clone repository: Clone this repository to a location of your choice.
git clone https://github.com/moosin76/mcp_server_mariadb_reader.gitModify the MCP settings file:
Example settings:
{
"mcpServers": {
"mcp_server_mariadb_reader": {
"command": "node",
"args": ["<클론된 저장소 경로>/build/index.js"], // 클론된 저장소 내 build/index.js 파일 경로
"env": {
"MARIADB_HOST": "YOUR_DB_HOST", // MariaDB 호스트 주소
"MARIADB_PORT": "YOUR_DB_PORT", // MariaDB 포트 번호 (예: "3306")
"MARIADB_USER": "YOUR_DB_USER", // MariaDB 사용자 이름
"MARIADB_PASSWORD": "YOUR_DB_PASSWORD", // MariaDB 비밀번호
"MARIADB_DATABASE": "YOUR_DEFAULT_DB" // (선택) 기본 데이터베이스 이름
},
"disabled": false,
"autoApprove": []
}
// 다른 MCP 서버 설정...
}
}caution:
You need to change <클론된 저장소 경로> part to the local path where you cloned the actual repository (e.g. C:/Users/YourUser/Documents/GitHub/mcp_server_mariadb_reader ).
You will need to modify the MariaDB connection information in env object to match your actual environment.
This repository contains the built files, so you don't need to follow the steps below to use the server. If you want to modify the source code ( src directory ) directly and apply the changes, follow these steps:
Installing dependencies (first time or when changing package.json ):
npm installBuild after fix:
npm run buildThis command compiles TypeScript code in the src directory into JavaScript code in build directory.
(Optional) Detect changes during development and build automatically:
npm run watchThis project is distributed under the [Enter License Information] license.
특정 테이블의 스키마(컬럼 정의)를 가져옵니다.
| Name | Required | Description | Default |
|---|---|---|---|
| database | Yes | 데이터베이스의 이름입니다. | |
| table | Yes | 테이블의 이름입니다. |
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. While it implies a read operation ('가져옵니다' - retrieves), it lacks details on permissions, rate limits, error handling, or response format. For a tool with no annotations, 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 that directly states the tool's purpose without unnecessary words. It is appropriately sized and front-loaded, 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?
Given the tool's low complexity (2 required parameters, no output schema) and high schema coverage, the description is minimally adequate. However, it lacks behavioral context (e.g., permissions, errors) and does not reference sibling tools, which could improve completeness for agent usage.
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%, with both parameters ('database' and 'table') clearly documented in the input schema. The description does not add any additional meaning beyond what the schema provides, such as format examples or constraints. Baseline 3 is appropriate when the schema handles parameter documentation effectively.
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: '특정 테이블의 스키마(컬럼 정의)를 가져옵니다' translates to 'Retrieves the schema (column definitions) of a specific table.' This specifies the verb ('가져옵니다' - retrieves) and resource ('스키마' - schema). However, it does not explicitly differentiate from sibling tools like 'list_tables' or 'query_table', which would require a 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 does not mention sibling tools like 'list_databases', 'list_tables', or 'query_table', nor does it specify prerequisites or contexts for usage. This leaves the agent without explicit direction on tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
접근 가능한 모든 데이터베이스 목록을 보여줍니다.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states the tool shows a list, implying a read-only operation, but does not disclose any behavioral traits such as permissions required, rate limits, pagination, or what 'accessible' entails. The description is minimal and lacks critical operational 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 a single, clear sentence that directly states the tool's purpose without unnecessary details. It is appropriately concise and front-loaded, though it could be slightly more informative to improve completeness 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's simplicity (0 parameters, no output schema, no annotations), the description is minimally adequate. However, it lacks details on behavioral aspects like what 'accessible' means or how the list is formatted, which would be helpful for an agent. The absence of an output schema means the description should ideally hint at the return format, but it does not.
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 0 parameters, and the input schema has 100% description coverage (though empty). The description does not need to add parameter semantics, so it meets the baseline for tools with no parameters. No additional parameter information is required or 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 clearly states the tool's purpose: '접근 가능한 모든 데이터베이스 목록을 보여줍니다' (shows a list of all accessible databases). It specifies the verb '보여줍니다' (shows) and the resource '데이터베이스 목록' (database list), but does not explicitly differentiate it from sibling tools like 'list_tables' or 'get_table_schema'.
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 does not mention any prerequisites, context for usage, or comparisons with sibling tools such as 'list_tables' or 'query_table', leaving the agent to infer usage based on tool names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
특정 데이터베이스 내의 모든 테이블 목록을 보여줍니다.
| Name | Required | Description | Default |
|---|---|---|---|
| database | Yes | 데이터베이스의 이름입니다. |
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 shows table lists but doesn't describe return format (e.g., array of names, pagination), permissions required, rate limits, or error conditions. This leaves significant gaps for a tool that likely interacts with a database system.
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 in Korean that directly states the tool's function. There's no wasted wording, though it could be slightly more structured (e.g., by front-loading the core action more explicitly).
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 annotations and no output schema, the description is incomplete for a database tool. It doesn't explain what the output looks like (e.g., list format, metadata included), error handling, or behavioral constraints. For a tool with one parameter but potentially complex database interactions, more context is 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?
Schema description coverage is 100%, so the schema already documents the single 'database' parameter thoroughly. The description adds no additional parameter semantics beyond implying the tool operates within a database context, which is already clear from the schema. Baseline 3 is appropriate when schema does the heavy lifting.
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: '보여줍니다' (shows/displays) + '모든 테이블 목록' (all table list) + '특정 데이터베이스 내의' (within a specific database). It specifies the verb (show/list), resource (tables), and scope (within a database), though it doesn't explicitly differentiate from sibling tools like 'list_databases' or 'get_table_schema'.
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 sibling tools like 'list_databases' (for listing databases instead of tables) or 'get_table_schema' (for detailed table info), nor does it specify prerequisites or exclusions for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
특정 테이블에서 데이터를 조회합니다 (제한된 행 반환).
| Name | Required | Description | Default |
|---|---|---|---|
| database | Yes | 데이터베이스의 이름입니다. | |
| limit | No | 반환할 최대 행 수 (기본값 100). | |
| table | Yes | 테이블의 이름입니다. |
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 mentions 'limited rows returned', which hints at a constraint, but fails to cover critical aspects like read-only status, potential permissions needed, error handling, or response format. This is inadequate for a query tool with zero annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence in Korean that directly states the tool's purpose and a key constraint ('limited rows returned'). It is front-loaded with no wasted words, making it highly concise and well-structured.
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 a database query tool with no annotations and no output schema, the description is insufficient. It lacks details on behavior, error cases, return values, and usage context, leaving significant gaps for an agent to operate effectively.
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 already documents all parameters ('database', 'table', 'limit') with clear descriptions. The description adds no additional meaning beyond what the schema provides, such as query syntax or examples, 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 ('query_table' translates to 'retrieve data from a specific table') and the resource ('table'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_table_schema' (which might return metadata) or 'list_tables' (which lists tables rather than querying data), missing full sibling distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 'get_table_schema' or 'list_tables'. It mentions 'limited rows returned', but this doesn't clarify usage context, exclusions, or prerequisites, leaving the agent without explicit direction.
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. Dates show when Glama detected each change.
v1.0.0get_table_schemalist_databaseslist_tablesquery_tableEach tool has a clearly distinct purpose with no overlap: get_table_schema retrieves column definitions, list_databases shows available databases, list_tables enumerates tables within a database, and query_table fetches data from a table. The descriptions clearly differentiate their functions, making misselection unlikely.
All tool names follow a consistent verb_noun pattern with snake_case (e.g., get_table_schema, list_databases, list_tables, query_table). The naming is predictable and readable throughout the set, with no deviations in style.
With 4 tools, the count is reasonable for a database reader server, but it feels slightly thin for covering all typical read operations. While core functions are present, additional tools like querying across tables or advanced filtering might enhance completeness without being excessive.
The tool set covers basic read operations (list databases, list tables, get schema, query data), but there are notable gaps for a database reader. Missing operations include querying with custom SQL, joining tables, or advanced filtering, which could limit agent capabilities in complex scenarios.
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