MCP Server

Michelangelo ships a remote MCP server — AI agents like Claude, Cursor, and any MCP-compatible client can connect directly and operate on the platform on behalf of a user: list projects, start AI generations, and wait for them to finish.

https://api.michelangelo.land/mcp

The server speaks Streamable HTTP (stateless) and implements the MCP authorization spec: when your client connects, it runs a standard OAuth 2.1 + PKCE flow automatically — Dynamic Client Registration, consent screen, token — with zero manual setup. No API keys to copy, nothing to install.

The MCP server is a pure client of the public API: every tool call goes through the same contract, the same per-user permissions (RLS), and the same server-side prompt evaluation. Anything the API can’t do, MCP can’t do either.

Connect your agent

Claude
Cursor
Other clients

Add a custom MCP connector with the server URL:

https://api.michelangelo.land/mcp

On first use, a browser window opens the Michelangelo consent screen. Sign in with Google, GitHub, an email code, or your password — the same Michelangelo account you use in the app — approve, and you’re connected. Claude handles registration and tokens for you.

Add to your mcp.json:

{
  "mcpServers": {
    "michelangelo": {
      "url": "https://api.michelangelo.land/mcp"
    }
  }
}

Cursor starts the OAuth flow on first use: approve the consent screen once and the token is managed for you.

Any client implementing MCP Streamable HTTP + OAuth 2.1 (RFC 8414 / RFC 9728 discovery) works:

Protected-resource metadata: https://api.michelangelo.land/.well-known/oauth-protected-resource
Authorization-server metadata: https://api.michelangelo.land/.well-known/oauth-authorization-server
Registration (DCR): POST https://api.michelangelo.land/auth/v1/oauth/clients/register

See Authentication for the full OAuth flow if you want to drive it manually.

Available tools

Tool	What it does
`whoami`	Identity behind the current token: user id, OAuth client id, scopes
`list_projects`	The user’s own Michelangelo projects (paginated with `cursor`); `visibility:'all'` also includes public community projects
`get_project`	A single project by numeric id
`create_job`	Start an async AI generation on an existing project — the prompt is evaluated server-side before acceptance
`get_job_status`	Point-in-time job status (`queued`, `running`, `succeeded`, `failed`)
`wait_for_job`	Polls a job until it finishes or `maxWaitSeconds` (default 50, max 600) elapses — returns `done:false` with the current status if still running, so the agent can simply call again

create_job accepts an optional model tier — light (faster, cheaper, for simple changes) or full (deepest reasoning, for complex builds). The tiers are vendor-neutral: the concrete model behind each tier is a server-side detail that improves over time without breaking your integration. If omitted, server-side prompt evaluation assigns the tier.

A typical agent flow

list_projects                      → discover the user's projects
create_job(projectId, prompt)      → 202, job queued (id returned)
wait_for_job(jobId)                → polls until done:true or time budget ends
  ↳ done:false? call again — the job keeps running server-side

Generations can take a long time (complex builds up to ~1 hour). wait_for_job never hangs forever: it waits up to your budget with exponential backoff, then hands control back with the current status. A failed job is a normal outcome, not a tool error — the agent reads job.error and can react (fix the prompt, retry).

Current limits

Project creation is not available via API/MCP yet — jobs run on projects the user already created in the Michelangelo app. create_job on a nonexistent project returns an error; use list_projects to discover valid ids.
Prompt evaluation quota applies to create_job (per-user, shared with the app). Exceeding it returns a rate_limited tool error with a Retry-After hint.
Scopes: tokens currently carry the email scope only; per-user RLS on the underlying data is what actually gates access.

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Connect your agent

Available tools

A typical agent flow

Current limits

​Connect your agent

​Available tools

​A typical agent flow

​Current limits

Connect your agent

Available tools

A typical agent flow

Current limits