**Pure Rust MCP Server** ShadowCrawl is a high-performance, Zero-Docker MCP server written in Rust. It serves as a 100% private, sovereign alternative to Firecrawl, Jina Reader, and Tavily. Unlike other scrapers, ShadowCrawl v2.3.0 runs as a single standalone binary with native Chromium control (CDP) and a Human-In-The-Loop (HITL) fallback system, ensuring you can bypass 99.9% of bot protections (Cloudflare, DataDome) without complex infrastructure. Why AI Agents love ShadowCrawl: - Zero-Docker & Zero-Config: No Redis, No Qdrant, No SearXNG. Single binary setup. - God-Tier Anti-Bot Bypass: Native Chromiumoxide (CDP) with JS-level stealth injection and HITL (Human-In-The-Loop) fallback for solving CAPTCHAs. - Internal Meta-Search: Parallel search across Google, Bing, DuckDuckGo, and Brave without external API keys. - Smart Ad-Blocking: Built-in high-speed aho-corasick engine to strip ads and trackers before extraction. - Semantic Memory: Embedded LanceDB for 100% local, private research history recall. - AI-Optimized Markdown: Delivers ultra-clean content stripped of "Buddhist Era" dates and web noise. Tools list: search_web — federated search (No API Key needed) search_structured — search + top result scraping scrape_url — single URL extraction scrape_batch — multi-URL parallel scraping crawl_website — bounded recursive crawling extract_structured — schema-driven extraction research_history — semantic recall from prior runs proxy_manager — proxy list/status/switch/test/grab operations non_robot_search — [NEW] The "Nuclear Option" for Boss-level anti-bots (LinkedIn/Cloudflare) with HITL.

AI agent security scanner — prompt injection detection, SQL injection, PII isolation, threat intel.

A MySQL-compatible MCP server for GitHub Copilot and LLMs.

Access PubMed's medical research database via the Entrez API for biomedical literature search and analysis.

AI-queryable MCP server for unified real-time air, sea, and space tracking — 25+ tools, agent skills, guardrails, and a 3D globe

MCP server for SLAM Gadget Shopify SQLite databases.

MCP server for Memento persistent memory system. Server name: memento. Tools: memento_mem_*. Includes AI skill for teaching agents.

URL slug generator API for AI agents. Convert any text into clean, URL-friendly slugs with accent transliteration, custom separators, and lowercase normalization. Tools: text_generate_slug. Use this for generating URL paths, file names, or database-friendly identifiers from user input or titles. Returns: {slug, original}. No API key required — x402 micropayment $0.001/call on Base L2.

Universal database MCP server connecting to MySQL, PostgreSQL, SQL Server, MariaDB,DM8,Oracle,not only provides basic database connection such as OAuth 2.0 authentication , health checks, SQL optimization, and index health detection

A production-ready URL shortener with click analytics and native MCP tools, built with FastAPI, PostgreSQL, and Next.js

Automatically extracts, processes, and indexes code snippets from GitHub repositories into a searchable vector database.

🧩 Build AI-powered MCP Tools with Azure Functions, Durable Agents & Cosmos vector search. Features orchestrated multi-agent workflows using OpenAI.

MCP server for executing Snowflake queries

A Snowflake MCP server — SQL queries, schema exploration, and data insights for AI assistants

محتوى تقني متميز في مختلف مجالات هندسة البرمجيات عن طريق تبسيط المفاهيم البرمجية المعقدة بشكل سلس وباستخدام صور توضيحية مذهلة

SoilWise is an AI + IoT-powered agricultural system that helps farmers make data-driven decisions for better yield, sustainability, and profitability. Using soil sensors, satellite imagery, and market data, the platform evaluates soil health, predicts rainfall trends, and recommends optimal crop and fertilizer plans — while also scoring farm-level financial and sustainability performance. It combines six smart modules: 🧠 Soil Analysis: Automated detection of soil type, pH, and nutrient balance. 🌾 AgriShield: Disease recognition and treatment recommendation using computer vision. 💧 IrrigAIte: Smart irrigation planning based on moisture data and local weather. 📈 Yield Predictor: ML-powered yield forecasting and credit scoring for farmers. 🤖 AgriChat: Conversational assistant for personalized advice. 📚 Research Checker: Validates agricultural research claims using AI evidence synthesis. 🧩 MCP Architecture Flow INPUTS ↓ [MCP Logic Layer] ↓ OUTPUTS Input Layer: 1.Soil sensor data (pH, moisture, nutrients) 2.Satellite imagery and weather forecasts 3.Farmer financial & field data (size, crop history) 4.Market data from open agri APIs MCP Logic Layer: 1.Data preprocessing & cleaning 2.AI models (soil classification, disease detection, rainfall prediction) 3.Predictive analytics for yield and credit scoring 4.Generative AI for chatbot and recommendations Output Layer: 1.Personalized crop and fertilizer plans 2.Financial risk and creditworthiness insights 3.Rainfall and yield forecasts (3-month horizon) 4.Interactive chatbot responses and visual dashboards ⚙️ What the MCP Does The MCP acts as the intelligent orchestration layer that links soil data, AI models, and farmer interfaces. It performs: 1.Real-time soil and satellite data processing 2.Cross-model inference for health and yield prediction 3.Dynamic decision generation (recommendations, warnings, or irrigation plans) 4.Data logging for continuous model improvement 🔗 How It Connects to the Client Frontend: Streamlit dashboard and SMS interface (via Africa’s Talking) MCP Server: Python backend (FastAPI + Streamlit) hosted on Azure Cloud MCP Node Data Pipelines: Pulls from satellite APIs (Google Earth Engine), local sensor input, and OpenAI for natural language reasoning Client Access: Farmers, agronomists, and cooperatives can log in or subscribe via mobile or web for real-time guidance 💡 Why It’s Useful or Creative 1.Transforms soil and environmental data into instant, actionable insights — no labs or delays. 2.Integrates AI, IoT, and financial scoring, giving farmers a holistic view of soil health + profitability. 3.Localized intelligence: Tailored to microclimates and soil types in Sub-Saharan Africa and North Africa (Tunisia pilot). 4.Scalable Design: Modular MCP architecture supports easy deployment across regions and languages. 📊 Financial & Credit Scoring Module a.Uses soil productivity metrics and yield forecasts to estimate farmer creditworthiness. b.Generates a SoilWise Credit Score to help farmers access loans or subsidies. Predictive metrics include: 1.Historical yield potential 2.Input efficiency 3.Sustainability index 4.Financial resilience model 🚀 Deployment a.Prototype Deployed: https://soilwise-prototype.streamlit.app/soilwise b.Backend Host: Azure Cloud with integrated MCP server c.Regions Tested: Western & Central Kenya (pilot), expanding to Tunisia for semi-arid adaptation d.Data Sources: Open Data Africa, Google Earth Engine, FAO Soil Database 📁 Repository 🔗 GitHub: https://github.com/antonie-riziki/SoilWise 🏷️ Tags / Categories #AI #Agritech #IoT #MCP #SoilHealth #ClimateResilience #SustainableFarming #CreditScoring

Bridges Apache Solr search indexes with vector embeddings for hybrid keyword and semantic document retrieval, enabling contextual searches against structured data repositories without direct database access

Read-only MCP server for Google Cloud Spanner

Query Spark SQL clusters via Thrift/HiveServer2. Works with Spark, EMR, Hive, Impala.

A portable accelerated SQL query, search, and LLM-inference engine, written in Rust, for data-grounded AI apps and agents.

MCP server providing AI assistants with safe, read-only SQL Server access

Provides secure SQLite database interaction with schema inspection and query execution capabilities for data analysis, content management, and application development workflows.