Blueprints

Blueprints are the rules and guidelines that shape your AI’s behavior, organized per tool. Keep the conventions, standards, and operating instructions your AI should follow in one place, and let your assistants read them over MCP instead of you re-explaining them in every conversation.

What are blueprints?

A blueprint is a piece of structured guidance — coding standards, architectural rules, review checklists, an API specification (OpenAPI, AsyncAPI, …), or any reference your AI should consult before it acts. Each blueprint has:

• Title — a human-readable name.
• Slug — a unique identifier within its project.
• Project — the grouping a blueprint belongs to (blueprints are addressed by project + slug).
• Content — the body of the blueprint (supports Markdown).
• Description — an optional short summary.
• Status — active or expired (archive without deleting).
• Metadata — optional custom key/value fields.

How blueprints differ from prompts and memory

Blueprints sit alongside Prompts and Memory, but each plays a distinct role:

• Prompts are reusable instructions you invoke to start or steer a task — the request you send.
• Memory is durable context your AI reads and updates as it learns — facts, decisions, and preferences that accumulate over time.
• Blueprints are the standing rules and guidelines that shape how the AI works — the conventions it should follow, organized per tool. They change deliberately, not as a side effect of a conversation.

Creating and editing blueprints

1. Navigate to Blueprints in the sidebar.
2. Click Create and fill in the details:
   • Project — the project this blueprint belongs to.
   • Slug — auto-derived from the title; must be unique within the project.
   • Title and optional Description.
   • Content — the guidance itself (Markdown supported).
   • Status — active by default.
   • Metadata — optional custom fields.
3. Click Save.

To edit, open a blueprint and choose Edit, make your changes, and save.

Note

A blueprint’s slug is unique per project. Two projects can each have a blueprint with the same slug.

Project scoping

Blueprints are organized by project, and a blueprint is addressed by its project plus its slug. Use projects to keep each tool’s or repository’s rules separate, and filter the Blueprints list by project, status, or a search term across title, description, and content.

Version history

Blueprints keep a content-version history. Each save snapshots the previous content as a numbered version, so you can:

• View version history — browse past versions, newest first.
• Inspect a version — open any earlier snapshot.
• Restore — roll a blueprint back to an earlier version. The pre-restore content is snapshotted as a new version first, so nothing is lost.

How AI tools read blueprints over MCP

When your AI assistant is connected to VibeXP through the MCP server, it can create and update blueprints directly, and discover them through semantic search.

• vibexp_io_create_blueprint — create a new blueprint (project, slug, title, content, optional type/status/metadata).
• vibexp_io_update_blueprint — update an existing blueprint, located by its project and slug.
• vibexp_io_search — find blueprints (and prompts, artifacts, and memories) by meaning; narrow to blueprints with the types filter.

// AI tool creates a blueprint over MCP
vibexp_io_create_blueprint({
  team_id: "<team-uuid-or-slug>",
  project_name: "my-api-project",
  slug: "coding-standards",
  title: "Backend Coding Standards",
  content: "# Standards\n\n- Wrap errors with context\n- ..."
})

Keep blueprints discoverable

Give blueprints clear titles and descriptions. The richer the wording, the more reliably semantic search surfaces the right rules when an assistant needs them.

Related features

• MCP Server Integration — connect your AI tools to VibeXP.
• Prompts — reusable instructions you invoke.
• Memory — durable context your AI reads and updates.