MCP server that gives Claude Code your team's engineering standards — git, Rails, testing, deployment, code review, frontend

Model Context Protocol (MCP) server for managing engineering standards, practices, and processes. This server provides AI assistants with structured access to your organization's engineering knowledge base through a standardized interface.

Groundrule: your engineering standards, taught to every coding agent and checked on every PR. CLI, checks, packs, MCP server and hooks.

MCP server for auditing Git repositories against software engineering standards

Your engineering standards, taught to every coding agent and checked on every PR. Open-source CLI, checks, packs, MCP server and hooks.

Deterministic knowledge layer for RAG systems. Open standard + search engine + semantic retrieval for AI-readable web content.

Open-source, AI-Native Generative Engine Optimization (GEO) & Technical Audit Suite. Like Lighthouse for Google SEO, open-geo is the open standard toolkit for Generative Engine Optimization (ChatGPT Search, Perplexity, Google AIO, Claude).

Cache-aware context engine & state governor for AI agents: standard MCP server, lossless pruning, prefix integrity.

Software engineering discipline grounded in named primary standards (OWASP, ITIL, IETF RFCs, ISO, BABOK, SBAR, and more), shipped as a Claude Code plugin and an MCP server.

Instagram bulk downloader: one engine behind a web UI, a CLI, and an MCP server. Standard library only apart from yt-dlp

Building performance standard (BPS) penalty engine and retrofit underwriting: DC BEPS, Energize Denver, NYC Local Law 97 and Boston BERDO, with open data, audited Excel models and an MCP server.

CLI + MCP sentinel for engineering standards — SOLID, testing, architecture, CI/CD — auto-tailored to your stack. Minimal MCP footprint (~200 tokens) via CLI-first design.

A production-grade Model Context Protocol (MCP) server engineered for LLM agents. Features highly modular, XML-isolated toolkit pipelines that eliminate formatting bias across custom data lookups and external API actions. Ready for standard local Stdio IDE workflows or scalable, multi-container SSE Docker network meshes.

MCP quality scanner and tool discovery for autonomous agents. Search 27,843+ indexed tools, check AEO (answer-engine-optimization) readiness scores, validate MCP server quality, find alternatives — the standard tool-validation layer before any agent calls

🚀 Kill the Junior AI Era. 🤖 Level up your AI code to Principal standards. No more sloppy lines or junior mistakes. Automated ESLint ✨ TypeScript 🔧 Prettier 🎨 SonarQube 🛡️ Security 🔒 Complexity 📊 in seconds. High-stakes quality, forced to ship only the best. ⚡🏆 Goodbye, bad code. Hello, Principal Engineer. 🚀✨

# Fashion E-Commerce Intelligence MCP Server Empower your AI agents, copilots, and assistants with vertical domain-specific fashion intelligence. Built by [alexgenovese.com](https://alexgenovese.com), this Model Context Protocol (MCP) server provides automated **SEO auditing, category trend research, DTC demand forecasting, CRM segment enrichment, and campaign creative generation**—designed natively for Cursor, Claude Desktop, Claude Code, and any MCP-compatible environment. --- ## ⚡ What It Does | Capability | Without this server | With Fashion MCP | |---|---|---| | **SEO Auditing** | Manual checklists, generic advice | Automated **0-100 score** across 5 dimensions with fashion-specific validation (fit, material, color, season, schema, OG, Twitter Cards). | | **Trend Research** | Generic search queries | Deep category-level trend intelligence (trending keywords, colors, silhouettes, price tiers, market fit, confidence). | | **Demand Forecasting** | Spreadsheets & gut feel | Explainable weighted-rule forecast model: `baseline × trend × media × retention × inventory × seasonality` with backtesting. | | **CRM Segment Enrichment** | Static email lists | Customer segments enriched with trending colors/silhouettes/keywords, audience clusters, and ready-to-send targeted messaging. | | **Campaign Creative** | Standard copywriters per channel | Platform-specific campaign themes, hooks, value propositions, and ready-to-use creative briefs grounded in real-time trend data. | --- ## 🔧 Exposed Tools - `product_seo_audit`: Audit any fashion product listing for SEO best practices, structured schema, meta tags, and fashion relevance (fit, size, material). - `fashion_trend_analysis`: Analyze fashion trend data mapped from search engine and social media queries. *(Receives search data input seamlessly, no API key required)*. - `fashion_category_demand_outlook`: Get a rapid, light demand pulse check for a specific product category in a target market and season. - `fashion_dtc_forecast_analysis`: Generate complete DTC demand forecasts with scenario planning (base/upside/downside) and stock-out/overstock risk analysis. - `fashion_customer_trend_enrichment`: Map trending keywords, colors, and silhouettes onto existing CRM customer segments for targeted email/SMS campaigns. - `fashion_campaign_theme_recommendation`: Recommend conversion-optimized ad campaign themes, hooks, and creative briefs for Meta, Google, TikTok, or Email. --- ## 🔐 Privacy & Technical Features - **No API Keys Required:** All analysis, SEO audits, demand forecasts, and CRM enrichment tools run locally with the data you supply. Upstream search data for `fashion_trend_analysis` uses your existing Tavily MCP server. - **Explainable Outputs:** Every tool returns explicit `assumptions[]`, `dataGaps[]`, and `confidence` scores, so your agent always understands the reasoning behind recommendations. - **Zero-Config Local Cache:** Uses a local SQLite persistence layer (`~/.fashion-mcp/store.db`) to cache snapshots, feature stores, and forecast actuals for backtesting. ## 💻 Local Installation To install this stdio-based server locally via Smithery: ```bash npx smithery install alexgenovese/ecommerce-fashion-market-analysis ```

Drive SolidWorks from Python / CLI / MCP: early-binding modeling, GB material library, assembly mates, exact interference, and GB-standard drawings - verified at every step.

Multi-persona engineering mentor with 64 specialized AI personas orchestrating collaborative guidance, session memory, and git-aware context

A standardized framework for enhancing how LLMs process and respond to prompts through composable decorators, featuring an official open standard specification and Python reference implementation. Claude Code plugin and MCP server integration.

Reads the conventions your repository already keeps, with a count behind every claim. Never blocks.

Open Standard & Model Context Protocol (MCP) Server for Acoustic Urban Health, Contemplative Travel & Sensory Impact Score (SIS v3.0 | DOI: 10.5281/zenodo.22756473)

Executable architecture governance. A CLI, MCP server and REST API that check a repository against Rego/OPA rules — and report a rule they could not evaluate as a failure, never a silent pass.

Token-optimized research engine & Agent Skill for 100% Open Access academic cybersecurity research (USENIX, NDSS, IACR, arXiv cs.CR, and NIST). Built for AI Agents & Security Analysts.

Ask which agent skills fit a task before you start it. One model call reads your task against the whole SKILL.md catalogue (193 skills bundled, plus yours) and names the few worth reading. CLI and MCP server for Claude Code, Codex, the Anthropic API or a local model. Python, standard library only.

For an organization to trust AI with its code, three things must hold — trust, traceability, and stability at scale. cladding wraps your AI coding agent: your intent goes in before it writes, and the result is verified against your spec after, so those three are earned, not assumed. First L4 implementation of the Ironclad standard.