AI content creation

Beyond serving content to client apps, Metabind lets AIs read and write content directly. The content-management MCP server exposes a set of tools that an AI can call to discover content types, query entries, draft new content, and update existing entries — under the same governance and audit trail as everything else.

What’s exposed

The content-management MCP server organizes its tools into three groups:

Group	Tools	Purpose
Context	`set_project`, `get_current_project`, `list_projects`	Tell the AI which project it’s working in
Discovery	`search_content_types`, `get_content_type`, `search_content`, `get_content`, `search_assets`	Read-only inspection of project content
Creation	`create_content`, `update_content`	Drafting and editing entries

A connected AI host (Claude Desktop, ChatGPT, etc.) calls these tools the same way it calls Metabind’s MCP App tools — JSON-RPC, schema-validated, audited.

Use cases

Drafting product descriptions with AI. The AI reads existing product entries, drafts copy in your house voice, creates a draft entry. A human editor reviews and publishes. Bulk migration from another CMS. A planning agent maps fields from the source CMS to your Metabind content type schema. A worker agent iterates source records and creates Metabind entries via create_content. Errors surface in the audit log for review. Content operations. “Tag every article published before 2024 as archive-candidate” or “Find any entries missing a hero image and flag them.” Reads + filtered updates on a schedule, with audit trail for compliance. Editor assistance. An editor in a custom in-app assistant asks the AI to “summarize this article in 3 sentences” or “rewrite the intro to match the tone of last month’s post.” The AI uses content tools to read context and update the draft.

Connecting

The content-management MCP server is exposed at:

https://mcp.metabind.ai/cms

Configure it in your MCP host the same way you configure project-scoped MCP Apps:

{
  "mcpServers": {
    "metabind-cms": {
      "url": "https://mcp.metabind.ai/cms",
      "headers": {
        "Authorization": "Bearer mb_..."
      }
    }
  }
}

The token’s scope determines what the AI can access — at the moment, it inherits the user’s organization role and the projects that role gives access to.

Governance and safety

All the platform-wide governance applies:

Schema validation. Every create_content and update_content call is validated against the content type’s schema before writing.
Permissions. The AI acts under the user’s role. A Viewer-scoped token can read but not write. An Editor-scoped token can draft. Publishing remains a human action by default.
Audit trail. Every call appears in the audit log with input, output, and the user identity behind the AI host.

For projects in regulated industries: consider keeping AI tokens scoped to Editor (drafts only) and requiring a human to publish.

Integrating into a custom assistant

If you’re building a custom assistant via the Assistant SDK, connect both your project’s MCP App server and the content-management MCP server. The AI will have tools for both rendering UI (Interactive Tools) and reading/writing content (CMS tools) in one conversation.

const assistant = createAssistant({
  project: { url: "https://mcp.metabind.ai/my-org/my-project", token: ... },
  additionalServers: [
    { url: "https://mcp.metabind.ai/cms", token: ... }
  ],
  llm: anthropic({ ... })
});

Managing content

Editor workflow in MCP App Studio.

Querying content

Read-only access via REST, GraphQL, SDKs.

Audit logs

Where AI content actions are recorded.

Tools and Types

The MCP App platform’s tool model.

Overview

Managing Content

Querying Content