The Best MCP Knowledge Base for ChatGPT & Claude (2026)

If you have tried to get ChatGPT or Claude to actually use your notes, you have run into the same wall everyone does: the assistant is brilliant in the moment and amnesiac between sessions. The fix that finally works in 2026 is a knowledge base with a Model Context Protocol (MCP) server — a store your AI can query as a tool, read from, and write back to. The question is no longer whether to use one. It is which one.

This is an honest comparison of the best MCP knowledge base options for ChatGPT and Claude — including where each one wins and where it doesn't. We build one of these tools (MDflow), so treat this as a vendor's map of the category, not a neutral referee's. We've tried to be fair about the trade-offs anyway, because the wrong tool for your situation will just frustrate you.

TL;DR — The best MCP knowledge base depends on your setup. Want a hosted MCP server plus a human editor and governance in one place, from any device? MDflow. Want a hosted AI-memory app? Hjarni. Want a free, open-source engine you can self-host? Basic Memory. Building your own app and need a memory API? mem0. Already living in a local vault? Obsidian with a community MCP plugin. All of them store Markdown you own — the differences are hosting, governance, and who the tool is really for.

What is an MCP knowledge base?

An MCP knowledge base is a collection of notes or documents that exposes a Model Context Protocol server so AI assistants can read and write it directly. MCP is the open standard — introduced by Anthropic in late 2024 and now supported across ChatGPT, Claude, Cursor, and most agent frameworks — that lets an assistant call external tools and fetch data in a uniform way. Point ChatGPT or Claude at an MCP knowledge base and, instead of you pasting context into the prompt, the assistant queries your notes itself: searching, opening documents, and often saving what it learned back.

That single capability changes the workflow. Your knowledge stops being something you re-explain every session and becomes a durable memory both you and your AI share.

Six things separate a good MCP knowledge base from a mediocre one, and they are the columns we'll compare:

• Built-in MCP server — does it ship one, or do you assemble it yourself?
• Read and write — can the AI update your notes, or only read them?
• Plain Markdown you own — portable files, or a proprietary store you can't easily leave?
• Works with both ChatGPT and Claude — and other MCP clients, not just one?
• Hosted vs. self-hosted — runs for you over HTTPS, or a process you run locally?
• Governance — version history, sharing, and encryption for knowledge you can trust?

The honest comparison

Here is how the leading options stack up on those six points. "Best for" is the verdict that matters most — pick the row that describes you.

The table hides nuance, so here is the honest read on each.

MDflow — hosted Markdown plus governance in one place

Best for: people who want their notes to be readable by ChatGPT and Claude and editable by a human, from any device, with the guardrails a shared knowledge base needs.

MDflow is a hosted Markdown workspace with a first-party remote MCP server at https://mdflow.cz/api/mcp. Because it's remote, there is nothing to install — you connect ChatGPT, Claude, Cursor, or Codex with a Personal Access Token (or OAuth where supported) and reach the same notes from your laptop or your phone. Agents can read and write: create, update, move, organize, and share documents through the MCP server and the HTTP API. Retrieval is a first-class tool — mdflow_get_context scores your folder descriptions first, so the assistant gets curated context, not a raw dump. The differentiator is that all of this sits alongside a genuine editor and real governance: automatic version history on every write path (so an AI edit is always reversible), public and private sharing, and optional client-side AES-256 encryption. Where MDflow is not the answer: if you specifically want everything to run offline on your own hardware with no hosted component, a self-hosted tool fits better.

Hjarni — hosted AI memory, done well

Best for: someone who wants a clean, hosted place for AI long-term memory and doesn't need a self-hosted option.

Hjarni is the closest direct comparison to MDflow's hosted model, and it's a solid product. It ships a hosted MCP server built in, stores plain Markdown organized in folders, works with ChatGPT and Claude, and leans into the "your AI reads your notes and writes back what it learned" loop — its most-called tool is the one that updates notes. It has folder-level instructions and team sharing. The main trade-off is stated plainly on their own site: it's hosted only, with no self-hosted path, and its center of gravity is AI memory rather than a full document workspace. If hosted memory is exactly what you want, it's an easy recommendation.

Basic Memory — the open-source engine

Best for: developers and tinkerers who want a free, local-first engine and are comfortable running it themselves.

Basic Memory is the strongest open-source option in the category. The engine is AGPL-3.0 licensed with roughly 3,000 GitHub stars, runs locally and offline for free, and stores plain Markdown files you can read, edit, and back up like any folder. It supports read-and-write over MCP with Claude, ChatGPT, and Cursor, and adds a typed knowledge graph and schema validation on top. There's a paid Cloud tier (around $15/seat/month, with private cross-device sync, audit logs, and team collaboration) for people who want hosting without self-managing it. The honest trade-off: the free tier is local-first, so reaching your notes from a phone or a shared machine means either running your own sync/server or paying for Cloud — the convenience a fully hosted service gives you by default.

mem0 — a memory API, not an end-user app

Best for: developers building their own AI product who need a memory layer, not individuals wiring up ChatGPT.

mem0 is excellent at what it does, but it's a different kind of tool. It's a memory layer for AI apps — an SDK and API that compress conversation history into retrievable memories, with enterprise features like SOC 2/HIPAA compliance and observability. It's aimed at developers embedding memory into a product, not at an end user who wants ChatGPT to read their personal notes. It doesn't store your knowledge as plain Markdown files you browse and edit; it's a memory store you query programmatically. If you're building an app, look closely. If you want a knowledge base you also read and write as documents, it's the wrong shape.

Obsidian — a local vault you extend yourself

Best for: people already committed to a local Markdown vault who are happy to assemble MCP support.

Obsidian is a beloved local Markdown editor, and its files are plain .md you fully own. But it has no official, hosted MCP server — MCP comes from third-party community plugins and servers you install and run yourself. That's fine if you're technical and work from one machine, but it's local-only and self-assembled: no built-in hosted access from your phone, no built-in sharing or version history the way a hosted service provides. Great vault; the MCP layer is DIY. (We wrote a fuller MDflow vs. Obsidian style comparison of hosted vs. local trade-offs elsewhere in the blog.)

Which setup is right for you

The choice comes down to a few honest questions.

1. Do you need access from more than one device? If yes — especially phones — a hosted, remote MCP knowledge base (MDflow or Hjarni) saves you from running and syncing a local server. If you only ever work from one machine, local tools (Basic Memory, Obsidian) are viable.
2. Do you want a human editor, or just AI memory? If you'll write and read documents yourself, you want a real editor with the MCP server attached (MDflow). If the notes are almost entirely AI-maintained memory, a memory-first tool (Hjarni, mem0) can be enough.
3. Is self-hosting a requirement or a nice-to-have? If running everything on your own hardware is non-negotiable, the open-source Basic Memory engine or an Obsidian plugin is the path. If you'd rather someone else run it reliably, choose hosted.
4. Are you an end user or a developer building a product? End users want a knowledge base app (MDflow, Hjarni, Basic Memory, Obsidian). Developers building their own AI app want a memory API (mem0).
5. How much does governance matter? Once an AI can write to your knowledge base, version history, sharing controls, and encryption stop being luxuries. This is where a hosted workspace with governance built in pulls ahead of a bare MCP server over a folder.

How MDflow fits

We built MDflow for the first row of that table: the person who wants their knowledge to be hosted, remote, plain Markdown that ChatGPT and Claude can read and write — with a real editor and real governance around it. That combination is the thing local-only and dev-only tools can't easily match.

What lines up today

A hosted remote MCP server, no install. MDflow's MCP server lives at https://mdflow.cz/api/mcp. Connect Claude, ChatGPT, Cursor, or Codex with a Personal Access Token — or OAuth where the client supports it — and you're reading and writing your notes from any device, no local process to babysit.

Curated retrieval, not a raw dump. mdflow_get_context takes a topic and scores your folder descriptions first, then names and titles, returning the most relevant Markdown bodies as readable context plus structured JSON. Folders nest to any depth and their descriptions cascade, so an agent reads the labeled chain of context before it opens a single document.

Producers, not just readers. Through the MCP server and the HTTP API, agents create, update, move, organize, and share documents — the write-back half of the loop, so your AI can capture what it learned and keep the knowledge base current.

Plain Markdown with raw .md twins. Every document is portable Markdown, and any shared link serves a raw .md twin with YAML frontmatter over open CORS — an assistant can fetch and cite a document in one request. No lock-in; export is just files.

Governance built in. Automatic version history on every write path (editor, API, and AI agent) with line diffs and one-click restore, public and private sharing, collections, anchored comments, and optional client-side AES-256 encryption. When an agent can write, every change is reversible.

Discovery for agents. MDflow ships an llms.txt index, an agent manual, an A2A agent card, and an OpenAPI spec — so assistants can find the surface, not just use it. See Markdown for AI for the full picture.

Where we're headed

This is direction, not a dated commitment, but the shape of our thinking: richer collection-level MCP so an agent can pull a whole cross-linked knowledge set at once; first-class document types and tags aligned with emerging standards like Google's Open Knowledge Format; and agent-assisted enrichment that proposes folder descriptions and cross-links for knowledge you already have. The Web Clipper already turns web pages into clean Markdown; capturing straight into an agent-ready knowledge base is the natural next step.

The bottom line

There is no single "best MCP knowledge base" — there's the best one for your setup. If you want hosted, plain-Markdown knowledge that ChatGPT and Claude can read and write, from any device, with an editor and governance in one place, that's the gap MDflow was built to fill. If you need self-hosting, Basic Memory's open-source engine is excellent. If you're building your own product, mem0 is a memory API worth a look. And if you already live in a local vault, Obsidian plus a community plugin will get you there with some assembly.

The category is young and moving fast, and the honest truth is that all of these tools beat the status quo of re-explaining yourself to your AI every morning.

Start free · Connect an AI agent · Read the API docs

Frequently asked questions

What is an MCP knowledge base?

An MCP knowledge base is a store of notes or documents that exposes a Model Context Protocol (MCP) server, so AI assistants like ChatGPT and Claude can read from it — and usually write back to it — during a conversation. Instead of pasting context by hand, the assistant queries the knowledge base as a tool. The best options store plain Markdown you own, work with more than one assistant, and support both reading and writing.

What is the best MCP knowledge base for ChatGPT and Claude?

It depends on what you need. MDflow is best if you want a hosted, remote MCP server plus a real editor and governance (version history, sharing, encryption) in one place, usable from any device. Hjarni is a close hosted alternative focused on AI memory. Basic Memory is best if you want an open-source engine you can run locally for free. mem0 is best for developers embedding a memory API into their own app rather than end users. Obsidian fits if you already live in a local vault and don't mind wiring up a community MCP plugin.

Do I need to run a local server to use an MCP knowledge base?

No. Local MCP servers (like the open-source Basic Memory engine or Obsidian community plugins) require you to run a process on your machine, which rules out phones and shared devices. Hosted, remote MCP knowledge bases like MDflow and Hjarni run the server for you over HTTPS, so you connect ChatGPT or Claude with a token or OAuth and reach the same notes from any device.

Can an MCP knowledge base write back to my notes, not just read them?

The good ones can. Read-only access lets an assistant answer from your notes; read-and-write access lets it capture what it learned, update documents, and maintain the knowledge base over time. MDflow, Hjarni, and Basic Memory all support write-back through MCP. When an agent can write, automatic version history matters — MDflow snapshots every change so an agent edit is always reversible.

Is Obsidian an MCP knowledge base?

Not out of the box. Obsidian is a local Markdown vault, and MCP support comes from third-party community plugins and servers rather than an official, hosted server. That works well if you are technical and stay on one machine, but it is local-only and self-assembled compared with a hosted service that ships an MCP server built in.

Further reading

• Anthropic — Introducing the Model Context Protocol
• Basic Memory — Open-source AI-native knowledge base
• Hjarni — Hosted memory for your AI
• MDflow — How to give ChatGPT and Claude access to your notes · MCP documentation · API documentation · Markdown for AI