Model Context Protocol
Omnibo MCP for any AI
Connect Omnibo to your workflow and generate images, video and audio directly from your prompts.
- ChatGPT
- Claude
- Grok
- Cursor
- Claude Code
- OpenClaw
- Hermes
How to connect
- 1
Open Claude settings
Launch the app or open claude.ai and go to Customize → Connectors.
Open Claude connectors - 2
Add the Omnibo connector
Click Add custom connector, name it Omnibo and paste the URL:
https://app.omnibo.ai/api/mcp - 3
Connect and sign in
Click Add → Connect, sign in with your Omnibo account, then ask Claude: “Generate a cinematic image of a lighthouse at dusk and show it to me.”
- 1
Open ChatGPT settings
Go to Settings → Apps & Connectors. If you do not see Create, turn on Developer mode under Advanced settings first.
Open ChatGPT connectors - 2
Create a connector
Choose Create, name it Omnibo, paste the URL and pick OAuth as the authentication:
https://app.omnibo.ai/api/mcp - 3
Connect and sign in
Finish the sign-in with your Omnibo account, then ask ChatGPT: “Generate a cinematic image of a lighthouse at dusk and show it to me.”
- 1
Open Cursor settings
Go to Cursor Settings → Tools & MCP → New MCP Server, or edit ~/.cursor/mcp.json directly. The one-click link installs it for you.
Add to Cursor - 2
Paste this config
Cursor signs you in through your browser. For a pasted key instead, use the header variant with a key from Settings → MCP & API keys.
https://app.omnibo.ai/api/mcpmcp.json{ "mcpServers": { "omnibo": { "url": "https://app.omnibo.ai/api/mcp" } } }mcp.json with an API key{ "mcpServers": { "omnibo": { "url": "https://app.omnibo.ai/api/mcp", "headers": { "Authorization": "Bearer omk_…" } } } } - 3
Sign in and ask
Click Needs login next to the server (or restart Cursor), sign in, then ask the agent: “Generate a cinematic image of a lighthouse at dusk and show it to me.”
- 1
Add the server from your terminal
Claude Code is configured on the command line — the CLI tab has the one-liner. In short:
Terminalclaude mcp add --transport http omnibo https://app.omnibo.ai/api/mcp
- 2
Check the URL it registered
It should be exactly this:
https://app.omnibo.ai/api/mcp - 3
Sign in and ask
Run /mcp inside Claude Code (or claude mcp login omnibo) to sign in, then ask: “Generate a cinematic image of a lighthouse at dusk and show it to me.”
- 1
Open the Control UI
Go to Settings → MCP → Add server. OpenClaw also has a CLI — see the CLI tab.
- 2
Add the Omnibo server
Name it omnibo, choose the streamable-http transport and paste the URL:
https://app.omnibo.ai/api/mcp - 3
Sign in and ask
Pick OAuth as the auth, sign in with your Omnibo account, then ask: “Generate a cinematic image of a lighthouse at dusk and show it to me.”
- 1
Add the server
Hermes is configured from its CLI or its YAML config — the CLI tab has the one-liner. The YAML form is:
config.yamlmcp_servers: omnibo: url: https://app.omnibo.ai/api/mcp auth: oauth - 2
Check the URL
It should be exactly this:
https://app.omnibo.ai/api/mcp - 3
Sign in and ask
Run hermes mcp login omnibo, sign in with your Omnibo account, then ask: “Generate a cinematic image of a lighthouse at dusk and show it to me.”
One-line install for CLI agents
For Claude Code, Codex, OpenClaw or Hermes, run this in your project. Showing Claude Code:
claude mcp add --transport http omnibo https://app.omnibo.ai/api/mcp
claude mcp add --transport http omnibo https://app.omnibo.ai/api/mcp --header "Authorization: Bearer omk_…"
Then run /mcp inside Claude Code (or claude mcp login omnibo) to sign in.
One-line install for CLI agents
For Claude Code, Codex, OpenClaw or Hermes, run this in your project. Showing Codex:
codex mcp add omnibo --url https://app.omnibo.ai/api/mcp
OMNIBO_API_KEY=omk_… codex mcp add omnibo --url https://app.omnibo.ai/api/mcp --bearer-token-env-var OMNIBO_API_KEY
Then run codex mcp login omnibo to sign in.
One-line install for CLI agents
For Claude Code, Codex, OpenClaw or Hermes, run this in your project. Showing Cursor:
# ~/.cursor/mcp.json
{
"mcpServers": {
"omnibo": {
"url": "https://app.omnibo.ai/api/mcp"
}
}
}# ~/.cursor/mcp.json
{
"mcpServers": {
"omnibo": {
"url": "https://app.omnibo.ai/api/mcp",
"headers": {
"Authorization": "Bearer omk_…"
}
}
}
}Then open Cursor Settings → Tools & MCP and click Needs login next to omnibo.
One-line install for CLI agents
For Claude Code, Codex, OpenClaw or Hermes, run this in your project. Showing Claude Code:
claude mcp add --transport http omnibo https://app.omnibo.ai/api/mcp
claude mcp add --transport http omnibo https://app.omnibo.ai/api/mcp --header "Authorization: Bearer omk_…"
Then run /mcp inside Claude Code (or claude mcp login omnibo) to sign in.
One-line install for CLI agents
For Claude Code, Codex, OpenClaw or Hermes, run this in your project. Showing OpenClaw:
openclaw mcp add omnibo --url https://app.omnibo.ai/api/mcp --transport streamable-http
openclaw mcp add omnibo --url https://app.omnibo.ai/api/mcp --transport streamable-http --header "Authorization: Bearer omk_…"
Then run openclaw mcp login omnibo to sign in.
One-line install for CLI agents
For Claude Code, Codex, OpenClaw or Hermes, run this in your project. Showing Hermes:
hermes mcp add omnibo --url https://app.omnibo.ai/api/mcp --auth oauth
hermes mcp add omnibo --url https://app.omnibo.ai/api/mcp --auth header --header "Authorization: Bearer omk_…"
Then run hermes mcp login omnibo to sign in.
Install as an Anthropic Skill
Download the skill bundle and drop it into your ~/.claude/skills/ directory. It teaches an agent when to reach for Omnibo, to read the model list before spending, to wait for a job and to hand you the file.
omnibo-skill.zipDownloadunzip omnibo-skill.zip -d ~/.claude/skills/
Install as an Anthropic Skill
Download the skill bundle and drop it into your ~/.claude/skills/ directory. It teaches an agent when to reach for Omnibo, to read the model list before spending, to wait for a job and to hand you the file.
omnibo-skill.zipDownloadunzip omnibo-skill.zip -d ~/.claude/skills/
Install as an Anthropic Skill
Download the skill bundle and drop it into your ~/.claude/skills/ directory. It teaches an agent when to reach for Omnibo, to read the model list before spending, to wait for a job and to hand you the file.
omnibo-skill.zipDownloadunzip omnibo-skill.zip -d ~/.claude/skills/
Install as an Anthropic Skill
Download the skill bundle and drop it into your ~/.claude/skills/ directory. It teaches an agent when to reach for Omnibo, to read the model list before spending, to wait for a job and to hand you the file.
omnibo-skill.zipDownloadunzip omnibo-skill.zip -d ~/.claude/skills/
Install as an Anthropic Skill
Download the skill bundle and drop it into your ~/.claude/skills/ directory. It teaches an agent when to reach for Omnibo, to read the model list before spending, to wait for a job and to hand you the file.
omnibo-skill.zipDownloadunzip omnibo-skill.zip -d ~/.claude/skills/
Install as an Anthropic Skill
Download the skill bundle and drop it into your ~/.claude/skills/ directory. It teaches an agent when to reach for Omnibo, to read the model list before spending, to wait for a job and to hand you the file.
omnibo-skill.zipDownloadunzip omnibo-skill.zip -d ~/.claude/skills/
The tools
Just describe it, Omnibo builds it
A look at the tools your agent can call, and what each one returns when you ask.
list_modelsLists every model this workspace can use right now, with its provider, the plan it needs, its credit rate and unit, and the parameters it accepts (sizes, qualities, durations, resolutions, actions, voices, formats).
job_statusReturns the current state of a generation started by generate_image, generate_video, generate_speech or transcribe_audio: pending, completed (with file_url and the credits charged) or failed (with the reason).
wait_for_jobBlocks for up to timeout_seconds (max 50) until a generation job is completed or failed, then returns it exactly as job_status would.
get_generationReturns one generation in this workspace by id — its prompt, model, status, credits charged and a file_url (a signed link valid for one hour).
list_generationsLists this workspace’s recent generations, newest first, with the same fields as get_generation.
balanceReturns the credits left in this workspace: `credits` (the internal unit every rate and charge in this server uses) and `display_credits` (the number the Omnibo app shows, credits ÷ display_divisor).
generate_imageGenerates an image from a prompt with one of the workspace’s image models and spends credits at the rate list_models quotes.
edit_imageEdits or remixes one or more reference images according to a prompt, with an edit-capable image model (supports_edit in list_models).
generate_videoGenerates a video from a prompt — optionally from a start frame, an end frame, reference images, clips or audio — with one of the workspace’s video models, at the per-second rate list_models quotes.
edit_videoRe-renders an existing clip according to a prompt (change the subject, the style, an object) with a model whose supported_actions include "edit".
extend_videoContinues an existing clip past its last frame according to a prompt, with a model whose supported_actions include "extend".
motion_control_videoApplies the movement of a driving clip (source_video_id or source_file_id) to a character still (reference_file_ids[0]) with a model whose supported_actions include "motion_control".
upscale_videoUpscales an existing clip (source_video_id or source_file_id) to a target resolution with a model whose supported_actions include "upscale".
analyze_videoAnswers a question about an uploaded or generated video — what happens, where something appears, a summary — with timestamps and evidence.
generate_speechReads text aloud with one of the workspace’s speech models (audio_kind "tts" in list_models) and a voice from its voices list, at the per-character rate list_models quotes.
transcribe_audioTurns an uploaded recording (source_file_id from upload_file) into text with one of the workspace’s transcription models (audio_kind "stt" in list_models), at the per-minute rate list_models quotes.
chat_completionSends a conversation (system / user / assistant messages) to one of the workspace’s chat models and returns the reply, the token usage and the credits charged at the rate list_models quotes.
upload_fileStores an image, video or audio file in this workspace so a generation tool can use it: as reference_file_ids or a start/end frame, as a clip to edit, or as a transcription source.
list_filesLists the files stored in this workspace — uploads and generated media — newest first, with a signed file_url for each.
How does MCP work?
The Model Context Protocol is an open standard that lets an AI assistant call tools on a server you choose. Omnibo runs such a server: connect it once, and every Omnibo model becomes a tool your assistant can use in the middle of a conversation.
Hosted assistants such as Claude and ChatGPT sign you in with your Omnibo account through OAuth — you approve what the connection may do on a consent screen, and you can revoke it any time. CLI agents and editors that prefer a pasted credential use an API key from Settings → MCP & API keys.
There is no separate pricing. A generation started from Claude spends the same credits at the same per-model rate as one started in the Omnibo app, from the same balance, and shows up in the same library.
Generation runs on Omnibo’s side. Your assistant gets a job id, waits for it, and then receives a link to the file — and, for images, the picture itself — which it shows you in the conversation.
Every creative model, inside your agent
The same catalogue the Omnibo app offers, read live: the agent lists it with one call and picks by cost and capability. 131 models from 13 providers today.
MiniMax M2.7 Highspeed · Claude Fable 5.1 · DeepSeek V4 Flash Vision (Experimental) · Gemini 3.7 Flash
All chat models →GPT Image 2.5 Flare · GPT Image 2.5 Sunburst · Grok Imagine 2.0 · Kling Image 3.0
All image models →ACE-Step · CassetteAI Sound Effects · Eleven Flash v2.5 · Eleven Multilingual v2
All audio models →Questions people ask about the Omnibo MCP
How does Omnibo connect to an AI assistant?
Through MCP, the Model Context Protocol. Omnibo runs a remote MCP server; you add its URL to your assistant, sign in with your Omnibo account, and the assistant can call Omnibo’s tools from inside a conversation.
Which assistants and agents are supported?
Any MCP client with remote-server support: ChatGPT, Claude, Cursor, Claude Code, Codex, OpenClaw and Hermes today, and anything that speaks the standard tomorrow. The MCP, CLI and Skill tabs above show the exact steps for each.
What models are available through the Omnibo MCP?
All 131 models in the Omnibo catalogue — chat, image, video and audio — with a new one appearing the day Omnibo adds it. The agent reads the live list with list_models, including each model’s credit rate.
What can I create from my assistant?
Images (generate and edit), video (generate, edit, extend, motion control, upscale), speech from text, transcripts from audio, and chat completions on any Omnibo model — plus uploads, so a reference image or a clip you already have can go in.
Do I need an API key?
No. Paste the URL and sign in — that is the whole setup for Claude, ChatGPT and the CLI agents. API keys exist for clients that prefer a pasted credential in a header, such as Cursor or a script, and you create them under Settings → MCP & API keys.
How does pricing work for the MCP?
It uses the same credit system as the Omnibo platform — there is no separate pricing. A generation from your assistant costs exactly what the same generation costs in the app, from the same balance.
How long does a generation take?
Images and speech usually finish within the call. Video takes minutes, so the tool returns a job at once and the assistant waits with wait_for_job until it is done — you can keep talking meanwhile.
Can I use my previous generations as input?
Yes. Every generation has a file id the assistant can pass to edit_image, extend_video or any tool that takes a reference, and upload_file puts your own files in the same place.
Can I use my own agent setup?
Yes. Any MCP client works, and the Skill tab has a bundle you can drop into your own agent’s skills folder so it knows how to use the tools well.
Your models, in your agent.
Create an account, open Settings → MCP & API keys, and paste one URL.