PostgreSQLデータベース管理機能を提供するモデルコンテキストプロトコル（MCP）サーバー。このサーバーは、既存のPostgreSQL設定の分析、実装ガイダンスの提供、データベースの問題のデバッグを支援します。
analyze_database )PostgreSQL データベースの構成とパフォーマンス メトリックを分析します。
構成分析
パフォーマンスメトリック
セキュリティ評価
最適化のための推奨事項
// Example usage
{
"connectionString": "postgresql://user:password@localhost:5432/dbname",
"analysisType": "performance" // Optional: "configuration" | "performance" | "security"
}get_setup_instructions ）PostgreSQL のインストールと構成に関するステップバイステップのガイダンスを提供します。
プラットフォーム固有のインストール手順
構成の推奨事項
セキュリティのベストプラクティス
インストール後のタスク
// Example usage
{
"platform": "linux", // Required: "linux" | "macos" | "windows"
"version": "15", // Optional: PostgreSQL version
"useCase": "production" // Optional: "development" | "production"
}debug_database ）一般的な PostgreSQL の問題をデバッグする:
接続の問題
パフォーマンスのボトルネック
ロックの競合
レプリケーションステータス
// 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 サーバー (ターゲット データベース操作用)
対象のPostgreSQLインスタンスへのネットワークアクセス
Smithery経由で Claude Desktop 用の PostgreSQL MCP サーバーを自動的にインストールするには:
npx -y @smithery/cli install @nahmanmate/postgresql-mcp-server --client claudeリポジトリをクローンする
依存関係をインストールします:
npm installサーバーを構築します。
npm run buildMCP 設定ファイルに追加:
{
"mcpServers": {
"postgresql-mcp": {
"command": "node",
"args": ["/path/to/postgresql-mcp-server/build/index.js"],
"disabled": false,
"alwaysAllow": []
}
}
}npm run dev - ホットリロードで開発サーバーを起動する
npm run lint - ESLint を実行する
npm test - テストを実行する
接続セキュリティ
接続プールを使用する
接続タイムアウトを実装する
接続文字列を検証する
SSL/TLS接続をサポート
クエリの安全性
SQLクエリを検証する
危険な操作を防止
クエリタイムアウトを実装する
すべての操作をログに記録します
認証
複数の認証方法をサポート
ロールベースのアクセス制御を実装する
パスワードポリシーを強制する
接続資格情報を安全に管理します
常に適切な資格情報を使用して安全な接続文字列を使用する
機密性の高い環境における本番環境のセキュリティ推奨事項に従う
データベースのパフォーマンスを定期的に監視および分析する
PostgreSQLのバージョンを最新に保つ
適切なバックアップ戦略を実装する
リソース管理を改善するために接続プールを使用する
適切なエラー処理とログ記録を実装する
定期的なセキュリティ監査と更新
サーバーは包括的なエラー処理を実装します。
接続失敗
クエリタイムアウト
認証エラー
権限の問題
リソースの制約
evals パッケージは mcp クライアントをロードし、index.ts ファイルを実行するため、テスト間で再構築する必要はありません。完全なドキュメントはこちら でご覧いただけます。
OPENAI_API_KEY=your-key npx mcp-eval src/evals/evals.ts src/index.tsリポジトリをフォークする
機能ブランチを作成する
変更をコミットする
ブランチにプッシュする
プルリクエストを作成する
このプロジェクトは AGPLv3 ライセンスの下でライセンスされています - 詳細については LICENSE ファイルを参照してください。
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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