ORANO MCP Server

A personal, read-only MCP (Model Context Protocol) server that lets a user's own AI agent (ChatGPT, Claude, Cursor, Ollama) read their ORANO library as grounded context.

Note: This public repository contains documentation, the MCP server manifest, and a reference implementation. The production MCP server runs as an authenticated endpoint mounted at /mcp on the ORANO backend (FastAPI + Postgres + pgvector). See the ORANO product site for the live endpoint and authentication flow.

What is ORANO?

ORANO turns saved Reels, TikToks, YouTube Shorts, and reference material into structured projects — summary, key takeaways, ordered tasks, research context, and a learning roadmap. Live on the iOS App Store (App Store listing).

A personal, read-only MCP server so a user's own AI agent can read that context is the differentiator.

MCP tools exposed

The ORANO MCP server exposes the following tools:

Tool	Description
list_projects	List the user's projects with optional status filters (active, completed, skipped, archived).
get_project	Return a single project's full structured understanding + summary + tasks + resources + research + roadmap.
get_project_context	Return only the requested context fields (summary, overview, caption, transcript, visual_context, links, tasks, roadmap, source, or raw_source) in structured, Markdown, or text output.
search_library	Search the user's projects by title, summary, source title, or URL.
read_memory_facts	Return curated memory facts (preferences, skills, goals) with confidence and freshness signals.
get_pending_handoffs	Retrieve projects explicitly sent from the ORANO app to a target agent. Each handoff is acknowledged once on read so concurrent polls do not duplicate.

Authentication

• Mechanism: Bearer personal API key, scope orano:read.
• No OAuth. Manual key creation only (per landing/mcp-access.html).
• Per-user budget: 240 calls per 60 minutes.
• Maximum active keys per user: 10.

MCP server manifest (server.json)

The canonical MCP server manifest follows the official MCP server.json schema:

{
  "$schema": "https://static.modelcontextprotocol.io/schemas/server.json",
  "name": "io.github.infotik/orano-mcp-server",
  "displayName": "ORANO",
  "description": "Personal, read-only MCP server that exposes the user's ORANO library (projects, tasks, research, roadmaps, memory facts) as tools for their own AI agent.",
  "version": "0.1.0",
  "repository": {
    "type": "git",
    "url": "https://github.com/infotik/orano-mcp-server"
  },
  "homepage": "https://oranoai.com/mcp",
  "categories": [
    "knowledge-management",
    "personal-assistant",
    "productivity",
    "second-brain"
  ],
  "tools": [
    { "name": "list_projects", "description": "List the user's ORANO projects with optional status filters." },
    { "name": "get_project", "description": "Return a single project's full structured understanding." },
    { "name": "get_project_context", "description": "Return only the requested context fields (summary, overview, transcript, visual_context, links, tasks, roadmap, source, raw_source)." },
    { "name": "search_library", "description": "Search the user's projects by title, summary, source title, or URL." },
    { "name": "read_memory_facts", "description": "Return curated memory facts with confidence and freshness signals." },
    { "name": "get_pending_handoffs", "description": "Retrieve acknowledged-once projects explicitly sent from the ORANO app to a target agent." }
  ],
  "transports": [
    { "type": "http", "endpoint": "https://api.oranoai.com/mcp/" }
  ],
  "authentication": {
    "type": "bearer",
    "scope": "orano:read",
    "user_specific": true,
    "rate_limit": "240 calls / 60 minutes / user"
  }
}

Reference implementation (Python)

The production server runs as part of the ORANO backend (FastAPI + SQLAlchemy + pgvector). The reference implementation pattern is:

from mcp.server.fastmcp import FastMCP
from mcp.server.auth.settings import AuthSettings
from mcp.server.auth.provider import AccessToken

mcp = FastMCP(
    name="orano",
    auth=AuthSettings(issuer_url="https://api.oranoai.com", required_scopes=["orano:read"]),
)

@mcp.tool()
async def list_projects(status: str | None = None) -> list[dict]:
    """List the user's ORANO projects."""
    ...

@mcp.tool()
async def get_project(project_id: str, fields: list[str] | None = None) -> dict:
    """Return a single project's full structured understanding."""
    ...

# ... plus get_project_context, search_library, read_memory_facts,
# get_pending_handoffs

The full production server (787 lines + 290 lines of auth/handshake helpers) lives in ExecutionOSBackend/app/mcp_server.py and is part of the private ORANO backend repository. Open-sourcing the full production code requires extracting the SQLAlchemy models + ingest services into a public package, which is on the product roadmap but not yet complete.

How to use

End users:

1. Install the ORANO iOS app from the App Store.
2. Sign in, save at least one Reel/TikTok/YouTube Short to generate your first project.
3. Open Settings → MCP access → Create new key (scope orano:read).
4. Connect your AI agent (ChatGPT, Claude, Cursor, Ollama) to your personal MCP endpoint with the key as a bearer token.

Privacy and trust

• The MCP server is read-only. It does not write to projects, sources, tasks, memory, or account data.
• The single state mutation is get_pending_handoffs acknowledging a queued app-triggered delivery by setting its delivery timestamp.
• No agent write access is promised; no one-click OAuth flow exists.

License

MIT — see LICENSE.

Links

• Product site
• MCP overview
• MCP access flow
• App Store listing
• Deck