提供 PostgreSQL 数据库管理功能的模型上下文协议 (MCP) 服务器。该服务器可协助分析现有 PostgreSQL 设置、提供实施指导以及调试数据库问题。
analyze_database ）分析 PostgreSQL 数据库配置和性能指标：
配置分析
性能指标
安全评估
优化建议
// Example usage
{
"connectionString": "postgresql://user:password@localhost:5432/dbname",
"analysisType": "performance" // Optional: "configuration" | "performance" | "security"
}get_setup_instructions ）提供分步 PostgreSQL 安装和配置指南：
特定于平台的安装步骤
配置建议
安全最佳实践
安装后任务
// Example usage
{
"platform": "linux", // Required: "linux" | "macos" | "windows"
"version": "15", // Optional: PostgreSQL version
"useCase": "production" // Optional: "development" | "production"
}debug_database ）调试常见的 PostgreSQL 问题：
连接问题
性能瓶颈
锁冲突
复制状态
// Example usage
{
"connectionString": "postgresql://user:password@localhost:5432/dbname",
"issue": "performance", // Required: "connection" | "performance" | "locks" | "replication"
"logLevel": "debug" // Optional: "info" | "debug" | "trace"
}Related MCP server: Postgres MCP Pro
Node.js >= 18.0.0
PostgreSQL 服务器（用于目标数据库操作）
对目标 PostgreSQL 实例的网络访问
要通过Smithery自动为 Claude Desktop 安装 PostgreSQL MCP 服务器：
npx -y @smithery/cli install @nahmanmate/postgresql-mcp-server --client claude克隆存储库
安装依赖项：
npm install构建服务器：
npm run build添加到 MCP 设置文件：
{
"mcpServers": {
"postgresql-mcp": {
"command": "node",
"args": ["/path/to/postgresql-mcp-server/build/index.js"],
"disabled": false,
"alwaysAllow": []
}
}
}npm run dev - 使用热重载启动开发服务器
npm run lint - 运行 ESLint
npm test运行测试
连接安全
使用连接池
实现连接超时
验证连接字符串
支持 SSL/TLS 连接
查询安全
验证 SQL 查询
防止危险操作
实现查询超时
记录所有操作
验证
支持多种身份验证方法
实现基于角色的访问控制
执行密码策略
安全地管理连接凭证
始终使用具有适当凭据的安全连接字符串
遵循敏感环境的生产安全建议
定期监控和分析数据库性能
保持 PostgreSQL 版本为最新版本
实施适当的备份策略
使用连接池实现更好的资源管理
实施适当的错误处理和日志记录
定期安全审核和更新
服务器实现了全面的错误处理：
连接失败
查询超时
身份验证错误
权限问题
资源限制
evals 包会加载一个 mcp 客户端，然后运行 index.ts 文件，因此测试之间无需重新构建。您可以在此处查看完整文档。
OPENAI_API_KEY=your-key npx mcp-eval src/evals/evals.ts src/index.ts分叉存储库
创建功能分支
提交你的更改
推送到分支
创建拉取请求
该项目根据 AGPLv3 许可证获得许可 - 有关详细信息，请参阅 LICENSE 文件。
Analyze PostgreSQL database configuration and performance
| Name | Required | Description | Default |
|---|---|---|---|
| connectionString | Yes | PostgreSQL connection string | |
| analysisType | No | Type of analysis to perform |
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 but only states what the tool does without detailing traits like whether it's read-only, requires specific permissions, has rate limits, or what the output format might be. 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 a single, efficient sentence that directly states the tool's purpose without any unnecessary words or fluff. It is appropriately sized and front-loaded, making it easy 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 complexity of database analysis, lack of annotations, and absence of an output schema, the description is insufficient. It doesn't explain what the analysis entails, what results to expect, or any behavioral traits, leaving the agent with incomplete context for effective tool 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?
The schema description coverage is 100%, meaning the input schema already documents both parameters ('connectionString' and 'analysisType') with descriptions and an enum. The description adds no additional meaning beyond what the schema provides, so it meets the baseline score of 3 for adequate but unenhanced parameter information.
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 with a specific verb ('analyze') and resource ('PostgreSQL database configuration and performance'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'debug_database' or 'get_setup_instructions', 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.
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 'debug_database' or 'get_setup_instructions'. It lacks any context about prerequisites, such as needing a valid connection string, or exclusions, leaving the agent without clear usage instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Debug common PostgreSQL issues
| Name | Required | Description | Default |
|---|---|---|---|
| connectionString | Yes | PostgreSQL connection string | |
| issue | Yes | Type of issue to debug | |
| logLevel | No | Logging detail level | info |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden but only states 'Debug common PostgreSQL issues', lacking details on behavior such as what the tool does (e.g., runs diagnostics, generates reports, modifies settings), permissions required, side effects, or output format. It doesn't disclose if it's read-only, destructive, or has rate limits, which is a significant gap for a debugging tool.
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, front-loaded and appropriately sized for its purpose. It avoids redundancy and is structured to convey the core idea 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 of debugging (potentially involving diagnostics, analysis, or fixes), no annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns, how it handles different issue types, or behavioral traits, leaving gaps that could hinder correct agent 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?
Schema description coverage is 100%, so the schema fully documents parameters like 'connectionString', 'issue' with enums, and 'logLevel'. The description adds no meaning beyond this, as it doesn't explain parameter interactions or provide examples. Baseline 3 is appropriate since the schema handles 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 'Debug common PostgreSQL issues' states a general purpose but lacks specificity about what debugging entails (e.g., diagnostics, fixes, logs) and doesn't clearly distinguish from sibling tools like 'analyze_database' or 'get_setup_instructions'. It's vague about the verb 'debug'—whether it analyzes, reports, or resolves issues.
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 like 'analyze_database' or 'get_setup_instructions'. The description implies usage for PostgreSQL issues but doesn't specify contexts, prerequisites, or exclusions, leaving the agent to infer 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.
Get step-by-step PostgreSQL setup instructions
| Name | Required | Description | Default |
|---|---|---|---|
| version | No | PostgreSQL version to install | |
| platform | Yes | Operating system platform | |
| useCase | No | Intended use case |
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 provides 'step-by-step instructions,' implying a read-only, informational output, but doesn't clarify aspects like response format, potential side effects, or error handling, which are important for a tool with parameters.
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 front-loads the core purpose ('Get step-by-step PostgreSQL setup instructions') with zero 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 tool's moderate complexity (3 parameters, no annotations, no output schema), the description is minimally adequate. It covers the purpose but lacks details on behavior, usage context, or output, leaving gaps that could hinder effective 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?
Schema description coverage is 100%, so the schema already documents all parameters (version, platform, useCase) with descriptions and enums. The description adds no additional parameter details beyond implying setup instructions, which aligns with the schema but doesn't enhance it, 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 action ('Get step-by-step... instructions') and resource ('PostgreSQL setup'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'analyze_database' or 'debug_database', which likely serve different purposes but aren't contrasted here.
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. The description lacks context on prerequisites, timing, or comparisons to sibling tools, leaving the agent without usage direction beyond the basic purpose.
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.
analyze_databasedebug_databaseget_setup_instructionsScored across 3 tools
Each tool has a clearly distinct purpose: analyze_database focuses on configuration and performance analysis, debug_database targets issue troubleshooting, and get_setup_instructions provides installation guidance. There is no overlap in functionality, making it easy for an agent to select the appropriate tool without confusion.
All tool names follow a consistent verb_noun pattern (analyze_database, debug_database, get_setup_instructions), using snake_case throughout. The naming is predictable and readable, with no deviations or mixed conventions.
With only 3 tools, the server feels thin for a PostgreSQL domain, which typically involves operations like querying, inserting, updating, or managing tables. While the tools cover analysis, debugging, and setup, the lack of core database interaction tools suggests an incomplete surface for typical agent workflows.
The tool set is severely incomplete for a PostgreSQL server, as it lacks basic CRUD operations (e.g., execute_query, create_table, insert_data) and management functions (e.g., list_tables, backup_database). This will cause significant agent failures when attempting to interact with the database beyond setup and diagnostics.
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