"How to Write Django Unit Tests" matching MCP servers:

A Model Context Protocol (MCP) server for developing Django applications. It exposes Django project information through MCP tools, enabling AI assistants to better understand and interact with Django codebases.

Provides a stateful Django shell environment that allows AI assistants to execute Python code directly against Django projects. Maintains persistent session state between calls, enabling iterative exploration of models, queries, and debugging.

Bridges Django applications with AI assistants by implementing the Model Context Protocol, enabling AI to interact with Django data and functionality through simple decorators.

A minimal MCP server with get_weather and create_ticket tools, used for testing MCP servers across protocol, unit, eval, transport, and auth layers.

Stateless MCP server for Django, enabling Django applications to interact via the Model Context Protocol.

Exposes Django runtime information (settings, apps, URLs, models, migrations) as MCP resources for AI agents to inspect a live Django project without static analysis.

Unit converter for AI agents, powered by the GNU units database — convert 3000+ units of measurement, evaluate compound unit expressions, reduce to SI base units, dimensional analysis. Offline, deterministic.

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

Enables local conversion of PDFs to Markdown, with automatic OCR fallback for scanned pages, image extraction, and page-by-page structure preservation.

Convert PDFs to structured JSON. Extract invoices, bank statements, contracts, and more. Pay per call via x402 USDC.

Enables an LLM or agent to drive a VUnit HDL unit-testing project end to end: list tests, compile, run, and inspect reports and per-test logs.

Detects tests that pass no matter what the code does, revealing vacuous tests that provide false confidence in coverage.

An MCP server that gives AI coding agents the ability to analyze a Python repository's test health: coverage, flaky tests, and ML-based pull-request risk prediction.

MCP server for writing Markdown report fragments to files with path safety and SHA-256 verification.

A write-only MCP server for Google Sheets with safety features including whitelist, dry-run, snapshot, and audit logging.

Enables AI development environments to call the Diffblue Cover CLI tool to automatically generate unit tests for Java code.

An MCP server that provides deterministic unit conversions backed by Pint, enabling exact unit conversions, compatibility checks, and parsing of quantities for LLMs.

Text-to-speech MCP server that enables AI assistants to read text aloud on the user's computer using Windows SAPI, with no API key or cloud service required.

Enterprise AI operator evaluation MCP server. 27 tools (22 read + 5 write) for measuring, benchmarking, diagnosing, and interv ening on how human operators use AI tools across 5 canonical metrics.

Enables developers to analyze Django projects for risky code patterns such as cascade delete impact, N+1 queries, unsafe migrations, and unscoped tenant reads through MCP tools and a CLI.