Database Query MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| DB_HOST | No | 数据库主机地址 | |
| DB_NAME | No | 数据库名称 | |
| DB_PORT | No | 数据库端口 | |
| DB_USER | No | 数据库用户名 | |
| DB_PASSWORD | No | 数据库密码 |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| query_mysqlA | 执行MySQL数据库查询(只读模式) |
| query_postgresqlA | 执行PostgreSQL数据库查询(只读模式) |
| query_mssqlA | 执行MSSQL数据库查询(只读模式) |
| query_oracleA | 执行Oracle数据库查询(只读模式) |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 4 tools
Each tool has a clearly distinct purpose targeting a specific database system (MSSQL, MySQL, Oracle, PostgreSQL). There is no ambiguity or overlap since each tool is dedicated to a different database technology, making tool selection straightforward for agents based on the target system.
All tool names follow a perfectly consistent verb_noun pattern: 'query_' followed by the database name (e.g., query_mssql, query_mysql). This uniform naming convention makes the tools predictable and easy to understand, with no deviations in style or structure.
With 4 tools, the count is reasonable for a database query server covering multiple systems. It might be slightly thin if additional database types (e.g., SQLite) are expected, but it effectively covers major databases without being overwhelming, making it well-scoped for its purpose.
The server provides read-only query capabilities for multiple databases, which is a clear and focused domain. However, there are notable gaps: it lacks write operations (e.g., insert, update, delete) and administrative functions (e.g., schema management, connection testing), limiting agents to read-only workflows and potentially causing failures for broader database tasks.