MCP
Model Context Protocol (MCP) is an open standard through which AI agents reach tools, APIs and data sources — from a local database to an external service.
SEO tools
Status: Under active development On-page SEO analyzer and site auditor. Crawls websites to surface metadata, content, and technical SEO issues, with AI/GenAI content readiness checks. Key features Crawlee + Playwright crawler extracting SEO meta tags, structured data (JSON-LD/microdata), and AI-indexing metadata Reports: Markdown SEO audit, title/meta-description issue lists, 404 and broken-link checks, empty-href scan Optional LLM-powered title/meta-description fix suggestions (OpenAI-compatible, incl. local models) MCP server exposing crawl and report tools to AI agents, plus an SEO-consultant persona Smart incremental crawling, sitemap discovery, and configurable rate limiting Dockerized stack; English and Czech report output Open source. The code is on GitHub and the running tool is at seo.ludekkvapil.cz — free to use, and a working sample of how I build.
OSINT tools
Status: Under active development AI-orchestrated OSINT investigation platform. Plans and executes multi-step intelligence cases — collecting evidence, generating reports, and maintaining a persistent knowledge wiki. Key features LLM-driven case orchestrator: auto-plans queries, runs tools, writes structured reports 15+ lookup scripts: RDAP, WHOIS, Shodan, ARES, LinkedIn/Apify, VIN, username recon, web fingerprinting, Czech public registries, media monitoring MCP-exposed tools: plan, investigate, run single tool, case status, wiki lookup/ingest Persistent LLM wiki for cross-case knowledge retention Optional Kali MCP pentest server for authorized active recon Dockerized stack with OpenClaw gateway; works locally or with cloud LLMs Open source. The code is on GitHub .
ClawBot – Agentic Ops Manager (IronClaw)
Status: Active development ClawBot is an agentic Ops Manager built on IronClaw. It orchestrates a team of specialized agents behind a unified MCP gateway, handling weekly SEO audits, daily OSINT monitoring, real-time support, and automatic knowledge base updates. It also manages the onboarding of new e-commerce stores and keeps Drupal and Hugo content in sync. Key features Unified MCP gateway with 20+ tools (RAG CMS, SEO crawler, OSINT, Sales) Scheduled tasks: weekly SEO audits, daily OSINT monitoring, and daily support gap analysis CMS/RAG integration: Drupal content synchronized with the knowledge base Automation: background tasks, reminders, and Canvas previews Docker-based stack for both local development and CI
Which Open-Source CMS Should You Choose for an AI Knowledge Base?
Most companies introducing an AI assistant over internal knowledge start with technology: which model, framework, or vector database to use. Those layers can be changed when needed. A knowledge base and CMS give an AI project a stable foundation: they determine where company knowledge is created, who maintains it, and how it stays current and approved. AI then works with information the company can trust. A CMS as a knowledge base, not only a website In an AI project, a CMS has a different role than on a website. It is where knowledge is created, structured, approved, and maintained, and where AI retrieves it. The selection criteria therefore differ: