"Microsoft Visio diagramming software" matching MCP servers:

Enables reproducible evaluation of AI coding agents by exposing repository inspection, code editing, test running, and deterministic verification through MCP tools.

Enables AI assistants to interact with iOS Simulators and Android Emulators, allowing autonomous app development, UI interaction, profiling, and debugging through natural language.

Enables AI agents to debug code inside VS Code by setting breakpoints, stepping through execution, inspecting variables, and evaluating expressions across multiple languages.

Gives your AI the super power of traveling back in time to find what went wrong and fix the bug timeline.

Enables AI coding assistants to scan an iOS, Android, Flutter, React Native or Expo project for App Store and Google Play rejection risks, returning deterministic findings that cite the guideline section, link to the official Apple or Google page and explain how to fix each problem. Nothing leaves the machine and no AI is used in the checks.

Lets any MCP client, such as Claude, browse attack suites and probes, run a full assurance assessment with all reports written out, send custom prompts or multi-turn histories and get leak, PII and refusal checks back for adaptive attacks, and read findings from an earlier assessment. This enables agent-driven red-teaming of a chatbot, RAG app or AI agent without leaving the chat client.

Provides an isolated workspace for testing candidate code, runs tests, and returns deterministic pass/fail verdicts. Enables automated grading of software engineering solutions by ensuring reproducible test runs.

Enables AI agents to perform software engineering tasks inside an isolated, deterministic sandbox—exploring repositories, reproducing failures, applying patches, running tests, and verifying solutions against hidden suites through MCP tools.

An intelligent MCP toolset for software testers that monitors code changes, analyzes test impact, recommends tests, and assesses risk, supporting multiple AI coding frameworks via dual transport modes.

Exposes the Maester security test framework for Microsoft 365/Entra/Defender as MCP tools, enabling AI agents to run tests, audit AI agents and MCP servers, and investigate security posture.

Enables AI agents to enforce a software delivery standard by validating artifacts against declarative rules, providing templates, glossary terms, and pass/fail verdicts with actionable blockers.

Lets assistants send the same test prompts to as many as four text models across OpenRouter, Amazon Bedrock, Google Vertex AI and Microsoft Foundry, then automatically grade each answer with a judge model, rule checks and a policy gate. Returns scored leaderboards with letter grades, per-response strengths and weaknesses, latency, throughput and estimated cost, plus downloadable PDF and CSV reports.

Performs vulnerability scans using Trivy to generate Software Bill of Materials (SBOM) in CycloneDX format. It enables automated security auditing and dependency tracking through the Model Context Protocol.

Enables AI clients to analyze software test results and defect records, producing explainable GO, CONDITIONAL_GO, or NO_GO release-readiness recommendations with deterministic risk scoring, failed-test retrieval, defect hotspot ranking, and regression test planning.

Enables interaction with ZenTao project management software through its REST API. Supports bug tracking, product management, and issue resolution with secure authentication for streamlined development workflows.

MCP server for standardizing software module development with tools for planning, code quality analysis, security scanning, performance evaluation, documentation generation, accessibility checks, and progress tracking.

Enables autonomous software development by managing requirements, architecture, task dependency DAGs, isolated worker workspaces, execution, contract and integration verification, and checkpoints. It provides observable evidence gates so AI workers can build and verify software against reproducible contracts.

Persistent visual cache for LLM-driven software development. Caches screenshots using perceptual hashing, vector search, and AX trees to prevent token overhead and visual hallucination loops.

Deterministic AST vulnerability remediation and formal verification for AI coding assistants using Microsoft Z3 SMT logic.

A universal AI-powered testing server built on the Model Context Protocol (MCP). Allows AI agents to inspect, execute, test, monitor, debug, and report on software projects.