# Frequently asked questions > Short answers about OpenMOCA: what MOCA stands for, whether it is a vector database, how it relates to RAG, hallucinations, OKF and MCP, and sovereign AI. Source: https://openmoca.org/docs/faq/ ## What is OpenMOCA? OpenMOCA is an open standard for portable grounding for AI. A MOCA package carries the knowledge an AI system answers from, how it is organised, and the original sources it came from, sealed by one digest and optionally signed. Open Readers in TypeScript, .NET and Python read, verify, search and cite it the same way, on a server or offline. ## What does MOCA stand for? Modular Ontology, Content & Attestation: knowledge in modular packages that can be assembled into larger ones, organised by an optional ontology, with attestations, signatures that prove who published it and who checked it. OpenMOCA is the open project: the specification, the Readers and the tools. ## How do you say OpenMOCA? "Open MO-kuh": MOCA is said like mocha, the coffee. Say it as one word, not as letters. The same goes for a `.moca` file: a "MO-kuh file". ## Is MOCA a vector database? No. A package is a file format for knowledge, and a Reader is a library that reads it. Search can run over the package's own text, over a sidecar index of vectors built for it, or through your own vector database: the Reader applies the same trust rules to each ([search and sidecar indexes](/docs/guides/search-and-indexes/)). ## Is MOCA an alternative to RAG? No. It is what you put into retrieval-augmented generation (RAG). A MOCA package gives a RAG pipeline knowledge with its version, its structure and its sources, so every answer can cite a passage that can be checked, and content that is out of date or for another audience is left out before the model sees it ([MOCA and RAG](/docs/moca-and-rag/)). ## Does MOCA stop hallucinations? It reduces the conditions for them, but no format controls what a model says. MOCA makes sure the model is given the right content (current, in scope, for the right audience, in the right order) and that every passage it is given carries a citation you can check against the original. Whether the model uses it faithfully is up to the model and your application, which can check its citations. ## Do I need a particular model, vector database or framework? No. A package names no model or service, and a Reader never calls one. The same package works with any model, any vector store and any agent framework. There are bindings for LangChain and LlamaIndex (Python), the Microsoft Agent Framework and Microsoft.Extensions.AI and VectorData (.NET), and an MCP server for any MCP client. ## Can it run offline, or on infrastructure we control? Yes. Readers are libraries that run in your process and never reach the network because a package names something. A package carries its sources, so quotes are checked offline, and signatures are verified against keys you list. Lexical (keyword) search needs nothing else; semantic search needs only an embedding model you run yourself ([sovereign and offline AI](/docs/sovereign-ai/)). ## What is the ontology, and do I need one? The optional structure layer is a small ontology in RDF (Turtle): topics, what is part of what, the order of steps, and what requires what. Every Reader understands a small core of W3C SKOS and Dublin Core terms, and answers questions about them the same way. The same file can carry any other vocabulary, such as OWL, SHACL, PROV-O or your own domain terms: Readers keep it in the package, covered by the digest, and leave its meaning to your application, which can act on it through its own retrieval hooks. You need it when order or prerequisites matter, as in procedures, regulations and courses. A package without it is complete ([ontology for AI retrieval](/docs/ontology/), [structure guide](/docs/guides/structure/)). ## How does MOCA relate to OKF and MCP? MOCA builds on the [Open Knowledge Format](https://github.com/GoogleCloudPlatform/open-knowledge-format) (OKF): the pages in a package are OKF documents, and MOCA adds the package around them. The Model Context Protocol (MCP) is how an agent calls tools; the MOCA MCP server lets any MCP client search and cite packages ([MOCA, OKF and MCP](/docs/moca-okf-mcp/)). ## Can AI agents use it? Yes. Any MCP client can search and cite packages through the MOCA MCP server, and there are bindings for the Microsoft Agent Framework, LangChain and LlamaIndex. The same package grounds every agent, with the same trust rules ([grounding for AI agents](/docs/ai-agents/)). ## How do I know who published a package, and whether anyone checked it? A package can be signed by its publisher, and individual pages can carry signed reviews. A Reader verifies both against a trust root you control, and every search result says who signed it and who reviewed it ([trust and provenance](/docs/trust-and-provenance/)). ## Which languages are supported? TypeScript (the reference Reader), .NET and Python. All three pass the same conformance corpus, so they reach the same conclusions about every package. The MCP server works with any client. ## Is it free, and who is behind it? Yes. The specification, Readers and tools are open source under Apache-2.0. OpenMOCA is maintained by PI & Mash Solutions Ltd, which holds the OpenMOCA trade mark on behalf of the project ([governance](/project/governance/)). ## Is it ready to use? It is at 0.6.0, before 1.0. The format is tested end to end and the three Readers agree on every case, but it may still change before 1.0 ([versioning](/spec/versioning/)). ## Where do I start? [Try OpenMOCA](https://try.openmoca.org) in the browser, follow the [walkthrough](/docs/walkthrough/), make a package with the [quickstart](/docs/quickstart/), or read one with the Reader for [TypeScript](/start/typescript/), [.NET](/start/dotnet/) or [Python](/start/python/).