"MCP servers built with languages other than Python and TypeScript" matching MCP servers:

Enables AI assistants and developers to analyze code for language-specific best practices and idiomatic patterns across programming languages, CI automation, and configuration formats.

A collection of Python-based Model Context Protocol servers that extend AI assistant capabilities with tools for calculations, AWS services (S3 and RDS), and PostgreSQL database operations.

Enables AI harnesses to maintain a persistent memory layer backed by a local SQLite file, providing MCP tools to add, search, deprecate, and synchronize facts without deleting history.

Provides a production-ready FastMCP Python server boilerplate with structured logging, rate limiting, and config validation for building MCP servers compatible with Claude and other agents.

Execute Python code via subprocess with timeout and error handling, returning stdout, stderr, and exit code.

A Model Context Protocol server that enables AI agents to query a Graphiti knowledge graph and pgvector document store for evidence-backed responses via hybrid search and RAG.

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

Implements a secure MCP server with API Key and JWT authentication, providing tools like echo, login, secure_action, and admin_action. Includes MCP Inspector integration for testing and debugging.

Python MCP server for SolidWorks automation with 109 tools covering the full CAD lifecycle. Enables AI-assisted design workflows through COM automation on Windows.

A quickstart template to build and deploy a remote MCP server on Azure Functions using Python, secured with keys and HTTPS.

Enables agents to review Python dependency requirements and source code against a modernization catalog, returning structured guidance, import-level suggestions, and compatibility floors. It also lists available codemod recipes and previews their transformations as unified diffs, all read-only and without installing packages or executing supplied code.

Enables building and deploying custom tool, resource, prompt, and interactive weather app endpoints on serverless infrastructure, with local debugging, built-in authentication, API Management security, and network isolation.

Enables developers to run and deploy a custom remote MCP server on Azure Functions that exposes MCP tools, resources, and prompts—such as snippet CRUD, QR code generation, code review checklists, and interactive app UIs—with built-in Microsoft Entra authentication. Supports local debugging and one-command cloud deployment, with optional API Management and VNET network isolation.

Enables developers to build, run locally, and deploy to the cloud a custom MCP server on Azure Functions that exposes tools, resources, prompts, and an interactive weather app over HTTP. Includes built-in authentication via Microsoft Entra, plus options for API Management and VNET network isolation.

Enables developers to build, run locally with debugging, and deploy to the cloud custom remote MCP endpoints hosted on Azure Functions, with built-in Microsoft Entra authentication. Sample projects demonstrate snippet CRUD and QR code tools, resource templates, prompt workflows, and an interactive weather demo UI.

Publishes sample Python implementations that expose tools such as snippet CRUD, QR code generation and batch operations, resources, and prompts like code review checklists and doc generation, plus an interactive weather app, to MCP clients. These can be run locally with debugging or deployed to Azure with built-in Microsoft Entra authentication.

Enables live runtime inspection of any Python application, allowing MCP clients to query state, evaluate expressions, inspect objects, and read source code while the app runs.

Enables developers to build and deploy custom tool, resource, and prompt integrations to the cloud using serverless functions, with local debugging and built-in authentication.

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

Sandboxed Python execution with automatic dependency management. Executes Python scripts in isolated environments (bubblewrap or Docker) with PEP 723 inline dependencies, preventing host pollution.

Enables AI coding assistants to browse Unreal Python API documentation, search classes and functions, and execute Python code in Unreal Editor.

Exposes 120+ Chinese frontier AI models (DeepSeek, Qwen, GLM, Kimi, Seedance, MiniMax, Hunyuan, CogVideoX) as MCP tools via one OpenAI-compatible endpoint: chat, image generation, free-model list, and a live first-party price leaderboard. Zero platform fee.

Python Docs MCP Server gives AI coding agents local access to the official Python standard library docs. It can look up docs by Python version, return specific sections, and keep answers compact for clients like Claude Desktop, Cursor, and Codex. It runs without API keys or a hosted service.

A Python MCP server that generates complete Vue 3 CRUD structures with Composition API, including components, services, stores, and routes.

A Python MCP server for Metabase that enables AI assistants to query databases, manage dashboards, and interact with your Metabase instance.

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

An MCP server that exposes Python debugging tools backed by debugpy, providing a focused debugging surface for local scripts.

A production-grade MCP server providing a persistent Python REPL with multi-session support, sandboxing, and timeout protection, enabling LLM agents to execute Python code across multiple turns with variables that persist between calls.

An MCP server that enables LLMs to automate browser interactions using Playwright to fetch real-time Israeli weather data.

A Model Context Protocol server that brings the capabilities of enigmapython library to LLMs, allowing them to encrypt and decrypt messages using historically accurate Enigma machine emulators