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Setting Up the Gumnut MCP Server

This guide walks you through configuring the Gumnut MCP server in popular AI tools.

Prerequisites

  1. A Gumnut account
  2. An MCP-compatible tool

Any MCP client

Gumnut runs a standard remote MCP server over HTTP, so a client that supports remote MCP servers can connect to it — provided it can also authenticate. Every request to the server carries your Gumnut credentials, so remote transport on its own isn’t enough: your client needs to support either the MCP OAuth flow or configurable request headers. The tools below have step-by-step instructions because they’re the ones people ask about most, not because they’re the only ones that work. To connect a client that isn’t listed, you need two things. The server URL:
Authentication, in one of two forms:
  • OAuth — if your client can sign in to remote MCP servers, point it at the URL and it will open a browser window to authorize your Gumnut account. Nothing else to configure.
  • An API key — if your client can’t do OAuth, or the sign-in flow gives you trouble, send the key as an Authorization header instead.
Most clients configure servers with JSON in roughly this shape:
Drop the headers block if you’re using OAuth. The exact keys vary between clients — some want an explicit transport field such as "type": "http" or "transport": "http" alongside the URL — so check your client’s documentation for its spelling.

Claude Code

Claude Code supports MCP servers natively through CLI commands or configuration files. When you connect, you’ll be prompted to authenticate with your Gumnut account via OAuth. Add the Gumnut MCP server with a single command:
To make it available across all projects, add the --scope user flag:

Using JSON Configuration

Alternatively, add the configuration directly to your ~/.claude.json file:
For project-specific configuration, create a .mcp.json file in your project root with the same structure.
You can also authenticate with an API key instead of OAuth by adding an Authorization header:
Or in JSON configuration:

Verify Installation

Check that the server is configured:
You can also use the /mcp command within Claude Code to see available servers and their tools.

Cursor

Cursor supports MCP servers through JSON configuration files. When you connect, you’ll be prompted to authenticate with your Gumnut account via OAuth.

Configuration File Location

  • Global (all projects): ~/.cursor/mcp.json
  • Project-specific: .cursor/mcp.json in your project root

Configuration

Create or edit the configuration file:

Using Cursor Settings UI

  1. Open Cursor and go to File > Preferences > Cursor Settings
  2. Select the MCP tab
  3. Click Add Server
  4. Enter the server details:
    • Name: gumnut
    • URL: https://api.gumnut.ai/mcp
    • Transport: http
You can also authenticate with an API key instead of OAuth by adding an Authorization header to the configuration:

Verify Installation

After configuration, go to Cursor Settings > MCP to see gumnut listed as a registered server. Click refresh if needed.

Claude Web (claude.ai)

Claude on the web supports remote MCP servers through the Integrations feature.
MCP integrations on Claude.ai require a paid plan (Pro, Max, Team, or Enterprise). Free accounts cannot connect to MCP servers—use Claude Code or Claude Desktop instead.

Setup Steps

  1. Go to claude.ai and sign in
  2. Click Customize then Connectors (or go directly to claude.ai/customize/connectors)
  3. Click + to add a new connector
  4. Enter the Gumnut MCP server URL:
  5. You’ll be prompted to authenticate with your Gumnut account via OAuth
  6. Save the connector

Using the Integration

Once configured, you can ask Claude to interact with your Gumnut library directly in chat:

ChatGPT

ChatGPT supports MCP servers through custom apps.
Gumnut will be added to the ChatGPT app store in the near future. In the meantime, you can connect manually using developer mode.

Setup Steps

  1. Open ChatGPT and go to Settings > Apps (or go directly to chatgpt.com/#settings/Connectors)
  2. Enable Developer mode
  3. Click Create app
  4. Configure the app:
    • Name: Gumnut
    • Server URL: https://api.gumnut.ai/mcp
  5. Expand Advanced settings. Under OpenID support, leave OIDC enabled unchecked, and under Default scopes, make sure openid is not selected (profile, email, and offline_access can stay selected).
    This step is required. With OIDC enabled, ChatGPT requests the openid scope during sign-in — but its connector is registered without that scope, so Gumnut’s authorization server rejects the request and you’ll see “There was a problem connecting Gumnut Photos. Try again later.” Leaving OIDC enabled unchecked stops ChatGPT from requesting openid, avoiding the mismatch.
    ChatGPT connector Advanced settings showing the OpenID support section with the OIDC enabled checkbox unchecked

    Advanced settings → OpenID support: leave OIDC enabled unchecked

    ChatGPT connector Default scopes panel with openid unchecked and profile, email, and offline_access checked

    Advanced settings → Default scopes: openid unchecked, with profile, email, and offline_access selected

  6. You’ll be prompted to authenticate with your Gumnut account via OAuth
  7. Save the app

Using Gumnut in ChatGPT

Once configured, you can ask ChatGPT to interact with your Gumnut photo library directly in chat.

Local and self-hosted models

You can use Gumnut with a model running on your own hardware:
  • The model generates the responses. It can run locally or in the cloud.
  • The MCP client is the app that connects to Gumnut and calls its tools on the model’s behalf.
Gumnut’s MCP server is cloud-hosted either way. Running the model locally doesn’t move your library or the API onto your machine — the client still reaches out to https://api.gumnut.ai/mcp over the network, and still authenticates as your Gumnut account. How you connect depends on which layers your local setup covers:

What to expect

Gumnut exposes a broad set of tools, and choosing between them is the hard part for a model:
  • Use a tool-calling model with a generous context window. Smaller models tend to pick the wrong tool, or ignore the tools entirely, as the list grows.
  • Image understanding needs more than a vision model. For view_asset to work, the model has to be vision-capable and the client has to pass image results from tools back to the model. Clients that only forward text will leave the assistant working from metadata alone.
  • Interactive MCP Apps widgets are client-side. Clients that don’t implement MCP Apps still get every tool — they render the results as text rather than as photo grids and album cards.

Troubleshooting

Having issues? See the MCP Troubleshooting guide for common problems and solutions.

Next Steps

Once configured, you can interact with your Gumnut photo library through natural language. See the MCP Overview for example interactions and available operations.