Self-hosted AI Starter Kit

The Pydantic Lod Test MCP server is a specialized, self‑hosted AI starter kit that stitches together a powerful low‑code workflow engine with cutting‑edge local AI capabilities. It addresses the common pain point of developers needing a single, reproducible environment to prototype and deploy AI‑driven automation without relying on external cloud services. By bundling a Docker Compose stack that includes n8n, Ollama, Open WebUI, Flowise, Qdrant, and PostgreSQL, the server gives teams an instant, fully‑functional playground where data ingestion, model inference, vector search, and workflow orchestration coexist on the same network.

At its core, the server exposes a set of MCP resources that let an AI assistant (e.g., Claude) query and manipulate the underlying services. The n8n instance serves as the workflow orchestrator, providing a visual interface to chain together triggers, actions, and AI nodes. Ollama hosts local large language models, enabling private, low‑latency text generation. Open WebUI offers a familiar ChatGPT‑style chat layer that can talk directly to the local LLMs and even invoke n8n workflows as agents. Flowise complements n8n by offering a no‑/low‑code agent builder that can be exported into the n8n ecosystem. Qdrant supplies a fast, vector‑search backend for retrieval‑augmented generation (RAG), while PostgreSQL acts as the relational store for structured data and workflow metadata.

Developers benefit from several key features. First, full local privacy: all data and model inference stay on the host machine, eliminating external dependencies. Second, GPU acceleration is supported out of the box for Nvidia GPUs via Docker profiles, giving near‑real‑time inference on powerful hardware. Third, the stack is extensible; any n8n node or Flowise component can be wrapped as an MCP tool, allowing the AI assistant to trigger complex pipelines with simple prompts. Fourth, the integrated vector store (Qdrant) and database (PostgreSQL) mean that a single MCP client can manage both structured data and unstructured embeddings, simplifying stateful AI applications.

Typical use cases include building automated customer support agents that pull from a local knowledge base, generating content or code by invoking custom n8n workflows that fetch and transform data, or creating a personal AI assistant that can query local documents via RAG while orchestrating background tasks. In research settings, the server allows rapid prototyping of multimodal or chain‑of‑thought workflows without provisioning cloud resources. The MCP integration makes it trivial to embed these capabilities into larger AI ecosystems, letting assistants act as glue between human intent and a rich set of local tools.