diff --git a/README.md b/README.md
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+++ b/README.md
@@ -1479,6 +1479,7 @@ Tools and integrations that enhance the development workflow and environment man
 - [get-tmonier/argot](https://github.com/get-tmonier/argot) [![get-tmonier/argot MCP server](https://glama.ai/mcp/servers/get-tmonier/argot/badges/score.svg)](https://glama.ai/mcp/servers/get-tmonier/argot) 🎖️ 🦀 🏠 🍎 🪟 🐧 - Guardrail that checks agent-written code against a repo's own git-history-learned patterns — flags foreign dependencies, reinvented functions, misplaced code, broken layering, and gamed tests. Exposes `voice_context` (the repo's familiar imports/idioms for a file, so agents write in-voice before generating), `check` (score a generated hunk against the repo's patterns), and `explain`. 100% local, no LLM, single Rust binary. `npm i -g @tmonier/argot`
 - [MikhailHal/ariadne](https://github.com/MikhailHal/ariadne) [![MikhailHal/ariadne MCP server](https://glama.ai/mcp/servers/MikhailHal/ariadne/badges/score.svg)](https://glama.ai/mcp/servers/MikhailHal/ariadne) ☕ 🏠 🍎 🪟 🐧 - Affected-test selection for Kotlin/Android projects: returns only the unit tests impacted by the current working-tree changes, so agents skip full test runs while iterating. Function-level static analysis via Kotlin Analysis API — call graph over functions, properties (initializers, delegates, accessors), constructors and callable references — with automatic multi-module and Android variant source-set discovery. Verified on Now in Android (34 modules, ~4s per query). `brew install mikhailhal/tap/ariadne`
 - [DanMat/create-packkit](https://github.com/DanMat/create-packkit/tree/main/mcp) [![DanMat/create-packkit MCP server](https://glama.ai/mcp/servers/DanMat/create-packkit/badges/score.svg)](https://glama.ai/mcp/servers/DanMat/create-packkit) 📇 🏠 - Scaffold modern npm packages, CLIs, services, and full-stack monorepos from your agent. Three tools: inspect the option schema, preview the generated file tree, and write the project to disk — optionally creating the GitHub repo and pushing to it, or merging into an existing clone without overwriting anything. Also available as a CLI and a web configurator. `npx -y packkit-mcp`
+- [gvasile29/qai-consultant](https://github.com/gvasile29/qai-consultant) [![gvasile29/qai-consultant MCP server](https://glama.ai/mcp/servers/gvasile29/qai-consultant/badges/score.svg)](https://glama.ai/mcp/servers/gvasile29/qai-consultant) 🐍 🏠 🍎 🪟 🐧 - Keyless local MCP server bringing ISTQB / OWASP / IEEE / ISO / EU AI Act QA standards into your AI client: standards-grounded retrieval, deterministic QA effort estimation, and automated QA document quality review (0-100 rubric) plus JUnit/CSV test-results flakiness analysis. `uvx qai-consultant-mcp`
 - [MGM-FALCON/quelllm-mcp](https://github.com/MGM-FALCON/quelllm-mcp) [![MGM-FALCON/quelllm-mcp MCP server](https://glama.ai/mcp/servers/MGM-FALCON/quelllm-mcp/badges/score.svg)](https://glama.ai/mcp/servers/MGM-FALCON/quelllm-mcp) 🐍 ☁️ 🍎 🪟 🐧 - MCP server for [quelllm.fr](https://quelllm.fr) — open-weights LLM catalog (250+ models with params, VRAM by quantization, tokens/sec, license). Tools: `list_models`, `get_model`, `compare`, `estimate_vram`, `estimate_cost` (API providers vs self-hosted hardware in EUR), `search_models`. Useful when picking a local model that fits your GPU/Mac.
 - [imqueue/mcp](https://github.com/imqueue/mcp) [![imqueue/mcp MCP server](https://glama.ai/mcp/servers/imqueue/mcp/badges/score.svg)](https://glama.ai/mcp/servers/imqueue/mcp) 📇 🏠 - Search @imqueue docs and scaffold typed services & clients for AI coding agents, access CLI and manage your service fleet.
 - [WCAG-Compliance/wcagc-mcp](https://github.com/WCAG-Compliance/wcagc-mcp) [![WCAG-Compliance/wcagc-mcp MCP server](https://glama.ai/mcp/servers/WCAG-Compliance/wcagc-mcp/badges/score.svg)](https://glama.ai/mcp/servers/WCAG-Compliance/wcagc-mcp) 🎖️ 📇 ☁️ 🏠 - Deterministic axe-core accessibility scanning (WCAG 2.1 AA, EN 301 549, PDF/UA) through a [wcagc](https://wcagc.com) account: scan any public URL or PDF, crawl a whole registered site, replay saved multi-step journeys, and read violation trends. Returns structured findings — rule, severity counts and the success criteria involved — so the assistant reasons over real results rather than guessing from markup. Every scan result carries an automated-coverage disclaimer instead of a score or a pass/fail verdict. Hosted at `https://mcp.wcagc.com/mcp`, or run it locally with `npx -y @wcagc/mcp`.