"Databases or database systems written in Python, like Postgres" matching MCP servers:

Validates AI-generated Python: syntax, lint, security scan and deterministic repair.

Call OpenAI Codex from Claude Code for independent second opinions, structured code review, and delegated coding tasks through a FastMCP plugin that drives the codex CLI safely.

MCP server providing automated code linting, rule explanations, and configuration templates for wemake-python-styleguide, with structured violation reports and offline rule database.

Enables AI agents to format SQL, explain queries in plain English, analyze schemas, build queries from natural language, and generate migrations, all without requiring a database connection.

This MCP server provides direct access to ruff linting, formatting checks, and ty type-checking for Python projects, with token-efficient, structured output.

Analyzes Python code and provides guided refactoring suggestions without automatically modifying code.

Enables AI assistants to safely explore, analyze, and maintain PostgreSQL databases with read-only mode by default, SQL injection prevention, query performance analysis, and optional write operations.

Exposes the 23 Gang of Four design patterns to AI coding agents for generation, canonical examples, AST-based detection, validation, and anti-pattern refactoring in Python codebases.

Enables deterministic static analysis of Python code, providing tools to inspect classes, functions, imports, dependencies, and more, without executing the code.

A Model Context Protocol server for deep codebase understanding of Python projects, focusing on data analysis and scientific computing. It provides architectural analysis, pattern detection, dependency mapping, test coverage analysis, and AI-optimized context generation.

Enables Python code linting by integrating the Model Context Protocol with tools like pylint and the OpenAI API. It allows for dynamic tool discovery and uses LLMs to orchestrate tool selection and provide refined analysis of linting results.

Enables secure read-only querying of multiple database types (MySQL, PostgreSQL, MSSQL, Oracle) through natural language. Automatically reads database configuration from project files and blocks any data modification operations for safety.

Enables comprehensive security vulnerability scanning and code quality analysis for Python applications. Provides detailed reports with scoring, actionable suggestions, and comparison tracking specifically designed for backend developers working with frameworks like Django, Flask, and FastAPI.

Exposes the structure and documentation of the openreview-py library, enabling LLMs to discover classes, functions, and signatures for accurate code generation. It provides tools for searching the API and retrieving detailed metadata without executing code or making external API calls.

Enables semantic code search across multi-language codebases using natural language queries, integrated with Qdrant vector database for fast, cached retrieval.

Provides tools for Python code navigation, analysis, and refactoring, including finding definitions, references, and symbol lists. It enables automated tasks such as renaming symbols and organizing imports to enhance AI-driven development.

Enables automated Python code quality checks including linting, complexity analysis, typo detection, structure validation, duplicate detection, and test coverage, with integration into Cursor IDE and CLI.

A production-ready MCP server that enables Claude to interact with local codebases through file operations, code analysis, and VSCode integration tools. It optimizes performance using LRU caching and asynchronous execution while maintaining local data privacy.

An MCP server that analyzes Python codebases using AST, stores code elements in a vector database, and enables natural language queries about code structure and functionality using RAG with Google's Gemini models.

Translates a lockfile diff into a human-readable upgrade plan. For every dependency bump (npm or PyPI) returns semver class, breaking changes from GitHub release notes, CVEs fixed in the range, migration guide links, and a clear per-package recommendation. Bulk tool ranks up to 50 package changes in parallel by risk (security > caution > review > likely-safe > safe).