12 deterministic database intelligence, AST SQL & schema reliability tools: natural language to SQL synthesizer, AST SQL injection & tautology auditor, query plan EXPLAIN optimizer, breaking schema migration differ, polyglot SQL transpiler (PostgreSQL/MyS

12 deterministic database intelligence, AST SQL & schema reliability tools: natural language to SQL synthesizer, AST SQL injection & tautology auditor, query plan EXPLAIN optimizer, breaking schema migration differ, polyglot SQL transpiler (PostgreSQL/MySQL/SQLite), composite B-tree index advisor, relational 3NF/BCNF normalization validat

The agentic meta-harness — freeze the model, evolve the harness. An open runtime that routes each query to the cost-optimal model, evolves its own harness (planner/context/reviewer/retry/tool/memory/score policy) and autonomously repairs code, then orches

MCP server for database connectivity. Multi-DB (PostgreSQL, MySQL, SQLite), connection management, schema introspection, query execution with token-optimized responses, rollback, history, elicitation for destructive ops, and prepared statements. Zero conf

Multi-query web search via SearXNG + dynamic page scraping with browser support + TOON encoding/decoding for LLM optimization

A lightweight MCP server for PostgreSQL that enables LLMs to safely query, analyze, and optimize database workloads.

AI-powered multi-cloud governance tool for Alibaba Cloud, Huawei Cloud & AWS. Natural language queries, idle resource detection, and cost optimization.

MCP server for PostgreSQL, MySQL, and SQLite schema analysis, index optimization, and query plan inspection

Security-focused MCP server for Claude Desktop - Read-only access to KnowBe4 GraphQL API with 92+ optimized queries, PII filtering, and audit logging

A Model Control Protocol (MCP) server for exploring the GitHub GraphQL schema and executing optimized queries. Provides AI assistants with efficient GitHub data retrieval capabilities through GraphQL.

Java-based MCP server for secure database query and management. Supports Oracle, MySQL, PostgreSQL with BYOK, CDC, lineage tracking, and SQL optimization.

High-performance MongoDB Model Context Protocol (MCP) server optimized for LLMs - enables AI agents to safely and efficiently query and manipulate MongoDB data with type safety, schema validation, and controlled access patterns

Python CLI and MCP server for Microsoft Fabric Data Warehouses and SQL Analytics Endpoints: administer, query, optimize, and secure them from your terminal or your AI agent

Python CLI and MCP server for Microsoft Fabric Data Warehouses and SQL Analytics Endpoints: administer, query, optimize, and secure them from your terminal or your AI agent.

An AI-native MCP server that replaces raw SaaS CRUD calls with token-optimized semantic queries — pre-filtered, compressed insights for Todoist, Linear, Spotify, and Strava instead of raw data dumps.

A Model Context Protocol (MCP) server optimized for India that provides comprehensive medical information by querying multiple authoritative medical APIs including FDA, WHO, PubMed, and RxNorm. Features India-first defaults for health statistics, Indian m

An enterprise-grade Model Context Protocol (MCP) Server v2 to dynamically manage, inspect, query, and optimize local SQLite databases directly through LLM chat sessions on Windows, macOS, and Linux.

The Magic Lane Model Context Protocol (MCP) Server enables any AI agent or application to become a geospatially intelligent assistant, capable of acting as a reliable companion for pedestrians, cyclists, and drivers alike. Built on top of Magic Lane's advanced mapping and routing technology, this TypeScript-based MCP server empowers AI agents to: - Understand and process geospatial data - Interpret user intent in a spatial context - Deliver accurate, contextually relevant navigation guidance By leveraging Magic Lane's global map data and real-time geospatial intelligence, the MCP Server enables deeply personalized navigation and trip-planning experiences through core tools: - Intelligent Search: find addresses, points of interest (POIs), or coordinates with intelligent search capabilities and POI category filtering. - Smart Routing: generate optimized routes between multiple waypoints with support for various transport modes, route preferences, and dynamic POI integration. - Round Trip Planning: create circular routes that start and end at the same location, perfect for jogging routes, cycling loops, or delivery runs. - Reverse Geocoding: convert geographic coordinates into detailed address information including street name, city, state, country, and postal code. - Isochrone Analysis: calculate reachability polygons showing areas accessible within specific time or distance constraints. - Route Visualization: create visual map images of calculated routes, isochrones, and round trips for immediate geographical context and presentation. - Geofence Management: create, manage, and query geographic boundaries for asset monitoring, location-based alerts, and spatial analysis with support for polygons, circles, and rectangles. - Interactive Map: Display an interactive vector tile map directly inside the conversation using MapLibre GL JS and MagicLane vector tiles. Supports location views, route visualization, isochrone overlays, round trip routes, search result markers, geofence areas, and custom coordinate paths — with one or many overlays per map and pan/zoom interactions. The map functionalities can be visualized as high-quality map images through our new beta feature to generate custom map views with markers, bounding boxes, or location-centered areas without route calculation, complementing route visualization for routes, isochrones, and round trips. Together, these capabilities enable AI agents to act as true geospatial copilots - understanding spatial context, anticipating user needs, and optimizing every journey with precision and ease.

# 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 ```

Zero-dependency, fully-reversible context compression for AI agents — cut LLM token usage up to 94% (JSON tabularization, log template mining, query-aware code skeletons). Stdlib-only, air-gap ready, built-in MCP server.

PostgreSQL query tuning for AI agents: your agent proposes, Postgres proves. MCP server, JSON CLI and agent skill.

Free, open-source SQL Server execution plan analyzer — cross-platform GUI + CLI with 30 analysis rules, missing index detection, SSMS extension. Built-in MCP server for AI-assisted plan review.

MCP server for FinOps cloud cost analysis using FOCUS billing data. Query AWS, Azure & GCP costs with AI assistants like Claude.

Read-only-by-default MCP server for one or many ClickHouse clusters: schema discovery, SELECT-only queries, EXPLAIN analysis, opt-in writes.

A security proxy and MCP firewall that protects databases from unsafe AI agent queries through real-time SQL inspection, policy enforcement, query blocking, and approval-based execution.

The database agent for Postgres and MySQL - Point it at PostgreSQL or MySQL and ask questions in plain English — schema exploration, query generation, slow query analysis, index recommendations, generated dashboards. Self-hosted and air-gapped.

Pre-flight query cost and result-size guardrails for AI agents across BigQuery, Snowflake, and Databricks

Source-available semantic and context layer, rule engine, and semantic sidecar for agentic AI and governed analytics. Compiles declarative YAML models into optimized SQL, KPIs, and semantic context across 8 SQL dialects.