PostgreSQL 데이터베이스 관리 기능을 제공하는 모델 컨텍스트 프로토콜(MCP) 서버입니다. 이 서버는 기존 PostgreSQL 설정 분석, 구현 지침 제공, 데이터베이스 문제 디버깅을 지원합니다.
analyze_database )PostgreSQL 데이터베이스 구성 및 성능 측정 항목을 분석합니다.
구성 분석
성과 지표
보안 평가
최적화를 위한 권장 사항
지엑스피1
get_setup_instructions )단계별 PostgreSQL 설치 및 구성 지침을 제공합니다.
플랫폼별 설치 단계
구성 권장 사항
보안 모범 사례
설치 후 작업
// Example usage
{
"platform": "linux", // Required: "linux" | "macos" | "windows"
"version": "15", // Optional: PostgreSQL version
"useCase": "production" // Optional: "development" | "production"
}debug_database )일반적인 PostgreSQL 문제를 디버깅합니다.
연결 문제
성능 병목 현상
잠금 충돌
복제 상태
// 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
노드.js >= 18.0.0
PostgreSQL 서버(대상 데이터베이스 작업용)
대상 PostgreSQL 인스턴스에 대한 네트워크 액세스
Smithery를 통해 Claude Desktop에 PostgreSQL MCP 서버를 자동으로 설치하려면:
npx -y @smithery/cli install @nahmanmate/postgresql-mcp-server --client claude저장소를 복제합니다
종속성 설치:
npm install서버를 빌드하세요:
npm run buildMCP 설정 파일에 추가:
{
"mcpServers": {
"postgresql-mcp": {
"command": "node",
"args": ["/path/to/postgresql-mcp-server/build/index.js"],
"disabled": false,
"alwaysAllow": []
}
}
}npm run dev - 핫 리로드로 개발 서버 시작
npm run lint - ESLint 실행
npm test - 테스트 실행
연결 보안
연결 풀링을 사용합니다
연결 시간 초과를 구현합니다
연결 문자열을 검증합니다
SSL/TLS 연결을 지원합니다
쿼리 안전
SQL 쿼리를 검증합니다
위험한 작업을 방지합니다
쿼리 시간 초과를 구현합니다
모든 작업을 기록합니다
입증
다양한 인증 방식 지원
역할 기반 액세스 제어를 구현합니다.
비밀번호 정책을 시행합니다
연결 자격 증명을 안전하게 관리합니다
항상 적절한 자격 증명을 사용하여 보안 연결 문자열을 사용하세요.
민감한 환경에 대한 프로덕션 보안 권장 사항을 따르세요.
정기적으로 데이터베이스 성능을 모니터링하고 분석합니다.
PostgreSQL 버전을 최신 상태로 유지하세요
적절한 백업 전략을 구현하세요
더 나은 리소스 관리를 위해 연결 풀링을 사용하세요
적절한 오류 처리 및 로깅 구현
정기적인 보안 감사 및 업데이트
서버는 포괄적인 오류 처리를 구현합니다.
연결 실패
쿼리 시간 초과
인증 오류
권한 문제
리소스 제약
evals 패키지는 index.ts 파일을 실행하는 mcp 클라이언트를 로드하므로 테스트 사이에 다시 빌드할 필요가 없습니다. 전체 문서는 여기에서 확인할 수 있습니다.
OPENAI_API_KEY=your-key npx mcp-eval src/evals/evals.ts src/index.ts저장소를 포크하세요
기능 브랜치 생성
변경 사항을 커밋하세요
지점으로 밀어 넣기
풀 리퀘스트 만들기
이 프로젝트는 AGPLv3 라이선스에 따라 라이선스가 부여되었습니다. 자세한 내용은 라이선스 파일을 참조하세요.
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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