local_pgsql

Name: local_pgsql
Author: z-waterking

@z-waterking

Repository

About local_pgsql

一个本地访问PGSQL的MCP Server

#database #analysis #pgsql

Basic information

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "github": {
      "command": "docker",
      "args": [
        "build",
        "-t",
        "mcp-pgsql-python-service",
        ".",
        "&&",
        "docker",
        "run",
        "-p",
        "8000:8000",
        "-e",
        "DB_HOST=${DB_HOST}",
        "-e",
        "DB_PORT=${DB_PORT:-5432}",
        "-e",
        "DB_NAME=${DB_NAME}",
        "-e",
        "DB_USER=${DB_USER}",
        "-e",
        "DB_PASSWORD=${DB_PASSWORD}",
        "mcp-pgsql-python-service"
      ],
      "env": {
        "GITHUB_PERSONAL_ACCESS_TOKEN": "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",
        "DB_HOST": "postgres",
        "DB_PORT": "5432",
        "DB_NAME": "postgres",
        "DB_USER": "postgres",
        "DB_PASSWORD": "postgres"
      }
    }
  }
}

Tools

No tools detected

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is local_pgsql?

local_pgsql is a database analytics and query automation toolkit that provides structured data access for analysis, reporting, and AI-augmented workflows. It is designed for developers and data analysts who need to explore PostgreSQL databases, run parameterized SQL queries, and perform statistical analysis through a modular tool interface.

How to use local_pgsql?

Initialize the MCP with a database connection string, register tools and prompts using the provided API, then call any of the built-in tools or prompts by name. For example, register a DatabaseManager for a PostgreSQL URI and enable tools such as list_tables, run_query, or get_summary_statistics.

Key features of local_pgsql

List all tables and get their schemas
Preview table contents with data sampling
Execute parameterized SQL queries
Compute numerical summaries and correlation matrices
Perform group-by aggregations with multiple functions
Run temporal aggregation and anomaly detection

Use cases of local_pgsql

Data exploration to rapidly understand dataset structure and content
Automated reporting with scheduled statistical summaries
Anomaly monitoring for real-time data quality checks
AI-augmented analysis via structured data access for LLMs

FAQ from local_pgsql

What database systems does local_pgsql support?

The toolkit includes a DatabaseManager interface for backend-agnostic operations, and the integration example shows a PostgreSQL connection string.

What tools are available for statistical analysis?

Tools include get_summary_statistics (mean, standard deviation), analyze_correlations (correlation matrices), and group_by_analysis (multi-function aggregations).

Does local_pgsql include built-in prompts?

Yes, it provides a basic SQL guide (basic_sql_guide) and analysis task templates (data_analysis_tasks).

How are tools registered and invoked?

Tools are dynamically registered via the @mcp.tool decorator and called using mcp.call_tool with the tool name and parameters.

What type of anomaly detection is supported?

It offers Z-score and IQR based anomaly detection through the detect_anomalies tool.

Comments

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