About Hubris
Hubris MCP connects Hubris, an OpenAI-compatible LLM gateway, to MCP clients that need model access and account usage details. It offers a model catalog, ruble pricing, and tools for text, embeddings, speech, images, video, and more.
Key features
- 01Browse and filter paginated model catalog
- 02Check pricing, balance, and spending
- 03Request chat, embeddings, and reranking
- 04Use decision, image, speech, and video models
- 05Transcribe audio and YouTube videos
- 06Access catalog resource and comparison prompt
Use cases
- 01Inspect available models and prices in an MCP client
- 02Call varied model capabilities through one gateway
- 03Check Hubris account balance and usage
MCP support
It offers a hosted Streamable HTTP endpoint and a local stdio server that forwards calls. MCP clients can access its tools, resources, and prompt; tool calls require a Bearer API key.
Configuration
Remote servers need no local install. Pick your client, then copy the config or install in one click.
https://api.hubris.pw/mcpclaude mcp add --transport http pw-hubris-hubris https://api.hubris.pw/mcp{"mcpServers":{"pw-hubris-hubris":{"type":"http","url":"https://api.hubris.pw/mcp"}}}Open in Cursor{"servers":{"pw-hubris-hubris":{"type":"http","url":"https://api.hubris.pw/mcp"}}}Open in VS Code{"mcpServers":{"pw-hubris-hubris":{"type":"http","url":"https://api.hubris.pw/mcp"}}}{"mcpServers":{"pw-hubris-hubris":{"serverUrl":"https://api.hubris.pw/mcp"}}}Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.
Tools
Details
From the project READMEHubris MCP
Model Context Protocol server for Hubris — an OpenAI-compatible LLM gateway with billing in rubles. Connect Claude Desktop, Claude Code, Cline, Cursor or any other MCP client to a single hosted server and get access to the model catalog, account balance and spending, and every kind of model in it: text, embeddings, rerankers, decision models, images, speech, transcription, video and YouTube transcripts.
There are two ways in: connect your client straight to the hosted endpoint, or run the stdio server in this repository, which declares the same tools locally and forwards calls to that endpoint. The second is what you want for clients that speak stdio only, and for sandboxes that build from a Dockerfile.
Listing the tools needs no credential; only calling one does. With HUBRIS_API_KEY set, the stdio server takes the tool list live from the hosted endpoint, so it never lags behind new tools; without a key it serves the list bundled with the package.
- Endpoint:
https://api.hubris.pw/mcp - Transport: Streamable HTTP
- Auth: Bearer API key (
sk-gw-..., create one at hubris.pw/keys) - Full docs: hubris.pw/docs/integrations/mcp
Tools
| Tool | Description |
|---|---|
models_list | Full catalog of active models, cursor-paginated. |
models_search | Filter by the tool that runs the model (tool), capability, price, context length. |
models_get_pricing | Pricing for a specific model, in rubles; voices for speech models, durations and resolutions for video models. |
balance_get | Current account balance. |
usage_get | Spending over a period: requests, tokens, rubles. |
chat_complete | LLM chat request — same as POST /v1/chat/completions. |
embeddings_create | Embedding vectors — POST /v1/embeddings. |
rerank | Order documents by relevance to a query — POST /v1/rerank. |
decisions_ask | Yes/no, choice and scale answers with probabilities — POST /v1/decisions. |
image_generate | Draw an image, returned as image content — POST /v1/images/generations. |
speech_create | Text to speech, returned as audio content — POST /v1/audio/speech. |
transcription_create | Speech to text from base64 audio — POST /v1/audio/transcriptions. |
video_create | Start a video generation — POST /v1/videos. |
video_get | Status of a video task; download links once it is ready — GET /v1/videos/{id}. |
youtube_transcribe | Transcript or summary of a YouTube video — POST /v1/video/transcriptions. |
Resources
| URI | Content |
|---|---|
hubris://catalog/models | Full model catalog as a single JSON document. |
hubris://docs/quickstart | Quickstart page. |
hubris://videos/{id}/content | A finished video, linked from video_get (hosted endpoint only). |
Prompts
compare-models {task}— ready-made prompt for comparing models for a given task.
Run locally (stdio)
npm install && npm run build
HUBRIS_API_KEY=sk-gw-... node dist/index.js
Or with Docker:
docker build -t hubris-mcp .
docker run -i --rm -e HUBRIS_API_KEY=sk-gw-... hubris-mcp
In an MCP client config:
{
"mcpServers": {
"hubris": {
"command": "node",
"args": ["/path/to/hubris-mcp/dist/index.js"],
"env": { "HUBRIS_API_KEY": "sk-gw-..." }
}
}
}
HUBRIS_MCP_URL overrides the endpoint if you need to point it elsewhere.
Connect
Claude Code
claude mcp add --transport http hubris https://api.hubris.pw/mcp \
--header "Authorization: Bearer sk-gw-..."
Claude Desktop
Claude Desktop only speaks stdio, so remote HTTP servers go through the mcp-remote shim. Add to claude_desktop_config.json:
{
"mcpServers": {
"hubris": {
"command": "npx",
"args": [
"-y",
"mcp-remote",
"https://api.hubris.pw/mcp",
"--transport",
"http-only",
"--header",
"Authorization:${AUTH_TOKEN}"
],
"env": {
"AUTH_TOKEN": "Bearer sk-gw-..."
}
}
}
}
Cline / Cursor / other clients
Any MCP client with Streamable HTTP transport support:
| Field | Value |
|---|---|
| Transport | Streamable HTTP |
| URL | https://api.hubris.pw/mcp |
| Auth header | Authorization: Bearer sk-gw-... |
Full connection guide (with client-specific caveats): hubris.pw/docs/integrations/mcp.
Security
The Bearer key grants access to your account balance. We recommend creating a dedicated key for MCP at hubris.pw/keys so it can be revoked independently of other integrations.
About Hubris
Hubris is an OpenAI-compatible LLM gateway for the Russian market — one API key, one ruble-denominated balance, 400+ models. hubris.pw
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.