Un servidor de Protocolo de Contexto de Modelo (MCP) que proporciona capacidades de ejecución directa de consultas a bases de datos PostgreSQL. Este servidor permite la ejecución de consultas SQL personalizadas en bases de datos PostgreSQL, con compatibilidad con consultas parametrizadas y tiempos de espera configurables. Este proyecto está diseñado específicamente para su uso con el IDE Cursor.
Node.js >= 18.0.0
Servidor PostgreSQL (para operaciones de base de datos de destino)
Acceso de red a instancias de PostgreSQL de destino
Related MCP server: PostgreSQL MCP Server
Para instalar PostgreSQL Database Query Server para Claude Desktop automáticamente a través de Smithery :
npx -y @smithery/cli install @1Levick3/postgresql-mcp-server --client claudeClonar el repositorio
Instalar dependencias:
npm installConstruir el servidor:
npm run buildAgregar al archivo de configuración de MCP:
{
"mcpServers": {
"postgresql-mcp": {
"command": "node",
"args": ["/Users/1Levick3/Desktop/postgresql-mcp-server/build/index.js"],
"disabled": false,
"alwaysAllow": [],
"env": {
"POSTGRES_CONNECTION_STRING": "postgresUrl",
"POSTGRES_SSL_CERT_PATH": "/Users/1levick3/Desktop/root.crt"
}
}
}
}npm run dev : inicia el servidor de desarrollo con recarga activa
npm run lint - Ejecutar ESLint
npm test - Ejecutar pruebas
Seguridad de la conexión
Utiliza agrupación de conexiones
Implementa tiempos de espera de conexión
Valida cadenas de conexión
Admite conexiones SSL/TLS
Bifurcar el repositorio
Crear una rama de características
Confirme sus cambios
Empujar hacia la rama
Crear una solicitud de extracción
Execute a custom SQL query against the database. WARNING: Use with care. Do not expose to untrusted input.
| Name | Required | Description | Default |
|---|---|---|---|
| connectionString | Yes | PostgreSQL connection string | |
| query | Yes | SQL query to execute | |
| values | No | Optional parameter values for the query | |
| timeout | No | Optional query timeout in milliseconds |
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 effectively communicates critical behavioral traits: that this is a potentially dangerous operation (executing arbitrary SQL), includes security warnings, and implies it can modify data. However, it doesn't specify what happens on success/failure, whether transactions are used, or what format results are returned in.
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 perfectly concise with just two sentences that each earn their place: the first states the core functionality, the second provides critical warnings. It's front-loaded with the main purpose and appropriately sized for the tool's complexity.
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?
For a potentially destructive database operation with no annotations and no output schema, the description provides adequate but incomplete context. It covers the dangerous nature and security considerations well, but doesn't explain what happens on execution (e.g., returns results, affects rows), error handling, or result formats, leaving gaps for the agent to navigate.
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?
With 100% schema description coverage, the schema already documents all 4 parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema, so it meets the baseline expectation but doesn't provide additional value regarding parameter usage or semantics.
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 ('execute') and resource ('custom SQL query against the database'), making it immediately understandable. It distinguishes what this tool does from potential alternatives by specifying it's for custom SQL execution rather than predefined operations.
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 clear contextual guidance with the 'WARNING: Use with care. Do not expose to untrusted input' statement, which helps the agent understand appropriate usage scenarios and security considerations. However, it doesn't specify when to use this tool versus alternative query methods or database tools since no sibling tools are listed.
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.
execute_custom_queryScored across 1 tool
With only one tool, there is no possibility of confusion or overlap between tools. The tool's purpose is clearly defined and distinct by default.
The single tool follows a clear verb_noun pattern (execute_custom_query), and there are no other tools to create inconsistency. The naming is straightforward and predictable.
A single tool for a PostgreSQL server is too few for the apparent scope, as it lacks basic CRUD operations, schema management, or other database functions. This minimal set limits functionality and forces all interactions through a generic query tool.
The tool surface is severely incomplete for a database server. There are no tools for common operations like listing tables, inserting data, or managing connections, leaving significant gaps that will cause agent failures in typical database workflows.
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