This is the MCP / AI reference for systemHUB — written for AI agents and developers. If you are a person looking for how-to help, use help.systemhub.com instead.

systemHUB Docs

Machine-readable documentation for connecting AI agents and technical users to systemHUB.

systemHUB is a platform for documenting, organising, and training teams on business systems — Systems (procedures/SOPs), Policies (compliance documents), and Trainings (learning modules). This documentation set describes how an AI agent (via the systemHUB MCP server) or a developer can connect to systemHUB, read a company’s content, and author or maintain it programmatically.

This is the AI-facing surface. If you’re a human looking for friendly, task-oriented help, use help.systemhub.com instead. The two layer together — they don’t replace each other.

Who this is for

• A prospect’s or customer’s AI assistant connecting to systemHUB via MCP, so it can find and use the company’s systems with no friction.
• Technical users / developers integrating with, or building on top of, the systemHUB MCP server.

Start here

1. Getting started — connect the systemHUB MCP server and make your first call.
2. Quick wins — verified copy-paste prompts for a newly connected agent.
3. Teach your AI — make it stick: the first-session prompt and the briefing your AI saves into its standing context.
4. Document lifecycle — how documents, states, and publishing work.
5. Roles & seats — how access is granted and counted.
6. Sharing & links — app links (login required) vs public share links (no login, machine-readable).
7. Authoring content — the formatting contract: what survives the editor and what gets stripped.
8. Errors & limits — known failure modes and validation limits, stated up front.
9. Tool reference — every MCP tool, its inputs, and what it returns, generated from the schema snapshot (the page states how old that snapshot is).

Guides (worked examples)

Task-oriented walkthroughs with request→response examples:

• Draft an SOP into systemHUB — orient, dedupe, create to the house format, attach templates/media, version.
• Audit access, sign-off & training — who can see what, who’s agreed, who’s completed training, what a person owns.
• Organise & maintain the library — folders, moves, states, tags, learning tracks, master templates.
• Publish your AI’s skills — give the skills your team has built a visible home: index by outcome, attach the file, and split shareable from business-wired.

What you can do through the MCP today

• Discover & read — browse the folder tree; search systems, policies, and trainings by name; read full document content, owners, tags, media, and attachments.
• Audit — see who a document is assigned to (members & roles), agreement/sign-off status, and training completion progress.
• Author & maintain — create, edit, move, and publish systems, policies, trainings, folders, learning tracks, tags, and media.

Known limits

These are documented honestly so a connecting agent doesn’t try what isn’t there yet:

• No seat provisioning via the MCP — inviting people and allocating licences happens in the app UI. (Document-level member/role assignment IS available — see manage_document_members.)
• Connections are admin-level. MCP access is available on admin seats, so a connected agent can read and write across the whole workspace. There’s no read-only mode and no lower-privilege connection — see Roles & seats before planning a team rollout. There’s also no admin view of which agents are connected, and no way to revoke one connection without affecting your others.
• Tokens expire after 180 days, with a reminder at day 144. Worth diarising if you’ve built anything that runs unattended — it will stop working on that date.
• No headless / service credential yet — the MCP connection is per-user OAuth, designed for a person’s AI client. Unattended automations (cron jobs, scheduled pipelines) have no non-expiring credential today; a long-lived API-key path is on the product roadmap.
• No workspace-wide usage analytics via the MCP. (Per-document activity, including views, IS readable — see get_document_activity_log.)
• No deletes, deliberately — an agent can create, edit, move and publish, but never delete. That’s a decision, not a missing feature: removing a document stays a human action in the app.
• But an agent can still replace a document’s contents, and the MCP has no way to read or restore an earlier version. Publishing creates versions, so history exists — it just isn’t reachable from here, and how much of a document a restore actually returns is currently unconfirmed (see Document lifecycle). Treat agent writes to approved documents as consequential: review before instructing, and cut a named version with publish_* before a substantial rewrite so there’s a labelled point to return to in the app.
• Images cannot be injected via the API — they’re uploaded through the editor. See Authoring content.

Recent capability changes are tracked in the changelog.

For AI agents reading this

Every page on this site is also servable as plain markdown — append .md to the page URL (e.g. /docs/getting-started.md). A machine-readable index lives at /llms.txt.