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

Local MCP server that connects VS Code agents like Cline to OpenAI and provides workspace file tools (read, write, list) with restricted file access.

A Django MCP server that exposes tools and resources to AI agents using simple decorators, with auto-discovery, type safety, and custom authentication.

A Django-native MCP application and tool registration framework inspired by Celery, providing async tool definitions, autodiscovery, and stdio/Streamable HTTP serving built on the official MCP Python SDK.

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

Enables AI assistants to perform Google searches with real, resolvable sources and transparent search terms.

Exposes the Grok Build CLI as MCP tools for Claude Code and other clients, enabling headless AI agents to perform tasks like code reviews, web research, and file operations with configurable permission levels.

Rewrites resumes to beat ATS screening (Workday, Greenhouse, iCIMS, Taleo) against a specific job description, with strict truthfulness guardrails — never invents dates, metrics, titles, or seniority. Pay-what-you-want access codes ($0 works).

Enables Claude Code to leverage local Ollama models for tasks like summarizing large files, reviewing diffs, semantic code search, and generating boilerplate or tests, while also supporting a fully offline mode.

Thin MCP server that enables AI agents to generate video, image, and audio content via the Deepy API using a personal API key, with safe generation flow and no business logic.

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

A low-hallucination vision MCP server that uses OpenAI-compatible multimodal models with structured prompts, confidence gating, and forced JSON to reduce false claims in image analysis.

Converts local images or remote image URLs to Base64 format via the MCP protocol, enabling AI assistants to process images.

An AI-powered QA automation tool that crawls web pages, generates Playwright-based pytest tests using an LLM, runs them, and automatically heals failing tests.

An MCP server that enables LLMs to detect cognitive biases and logical fallacies in text, list supported biases, and provide links to relevant psychology tests.

Enables offline local LLM analysis of text data via MCP, taking raw text and an instruction to return analysis results without cloud calls.

Simulate hundreds of AI agents to predict how communities react to events and policies. Upload any document (PDF, Markdown, text) and DeepMiro spawns a diverse swarm of AI agents that debate, share, and form opinions — then delivers a calibrated prediction report. Free and open-source.

Enables management of TestMu AI test projects, test cases, test runs, and integration with Jira, HyperExecute, and AI insights through natural language.

Enables AI assistants to read ICTExam exams, gradebooks, and per-question item analysis, and optionally parse question papers and publish or unpublish exams when write access is enabled.

A lightweight, zero-dependency Python MCP server that exposes a single do_everything tool. Any AI agent that supports MCP can use it to offload all language-model work to a local OpenAI-compatible endpoint, reducing cost on the agent's primary LLM.

AI development observability platform. It silently captures structured data from vibe coding sessions via MCP and codifies deviation patterns into project rules to make AI write better code. 100% local and zero runtime dependencies.