Install Hugging Bay in your agent | Hugging Bay

Install in your agent

Copy-paste setup for Claude Code, Claude Desktop, Codex, Cursor, Gemini CLI, VS Code, Windsurf, Hermes Agent, OpenClaw, LangChain, LlamaIndex and the OpenAI Agents SDK.

Pick your agent, copy and paste, and you are set. No account or key.

These setups use the four core tools: search models, check what fits your computer, read one model's files and hashes, and get a plain answer on whether to serve a file. All four only read. For board, request and plan tools, change profile=core to profile=discovery.

Claude Code

Connected in our test on 2026-11-05

Paste the command in your terminal. It adds Hugging Bay for all your projects.
Optional: add the skill so Claude knows when to use it.
Run /mcp in Claude Code to see hugging-bay connected.

Add the server

claude mcp add --transport http --scope user hugging-bay 'https://huggingbay.xyz/api/mcp?profile=core'

Add the skill (optional, macOS and Linux)

d="$HOME/.claude/skills/hugging-bay-models"; mkdir -p "$d/scripts" && curl -fsS https://huggingbay.xyz/skills/hugging-bay-models/SKILL.md -o "$d/SKILL.md" && curl -fsS https://huggingbay.xyz/skills/hugging-bay-models/scripts/hb.py -o "$d/scripts/hb.py"

Claude Code setup guide

Claude Desktop

Not tested by us yet

Open Settings, then Connectors, and choose Add custom connector.
Name it hugging-bay, paste the URL, and leave sign-in off.
On a plan without custom connectors, use the config file instead and restart Claude. It needs Node.js 22 or later.

Connector URL

https://huggingbay.xyz/api/mcp?profile=core

Config file on macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "hugging-bay": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote",
        "https://huggingbay.xyz/api/mcp?profile=core"
      ]
    }
  }
}

The bridge listed the tools in our test; not run inside Claude Desktop

Config file on Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "hugging-bay": {
      "command": "cmd",
      "args": [
        "/c",
        "npx",
        "-y",
        "mcp-remote",
        "https://huggingbay.xyz/api/mcp?profile=core"
      ]
    }
  }
}

Claude Desktop setup guide

Codex CLI

Config accepted, not connected in our test

Paste the command, or add the block to ~/.codex/config.toml.
Start Codex and run /mcp to see hugging-bay.

Add the server

codex mcp add hugging-bay --url 'https://huggingbay.xyz/api/mcp?profile=core'

Or ~/.codex/config.toml

[mcp_servers.hugging-bay]
url = "https://huggingbay.xyz/api/mcp?profile=core"

Add the skill (optional, macOS and Linux)

d="$HOME/.codex/skills/hugging-bay-models"; mkdir -p "$d/scripts" && curl -fsS https://huggingbay.xyz/skills/hugging-bay-models/SKILL.md -o "$d/SKILL.md" && curl -fsS https://huggingbay.xyz/skills/hugging-bay-models/scripts/hb.py -o "$d/scripts/hb.py"

Codex CLI setup guide

Cursor

Not tested by us yet

Open the install link and confirm in Cursor.
Or merge the JSON into ~/.cursor/mcp.json (all projects) or .cursor/mcp.json (one project).
Check Settings, Tools & MCP: hugging-bay should be on.

Add to Cursor

mcp.json

{
  "mcpServers": {
    "hugging-bay": {
      "url": "https://huggingbay.xyz/api/mcp?profile=core"
    }
  }
}

Cursor setup guide

Gemini CLI

Connected in our test on 2026-11-05

Paste the command. It adds Hugging Bay for all your projects.
Gemini turns MCP off in folders you have not trusted. Trust your project folder when it asks.
Run /mcp in Gemini to see hugging-bay connected.

Add the server

gemini mcp add --scope user --transport http hugging-bay 'https://huggingbay.xyz/api/mcp?profile=core'

Or ~/.gemini/settings.json

{
  "mcpServers": {
    "hugging-bay": {
      "url": "https://huggingbay.xyz/api/mcp?profile=core",
      "type": "http"
    }
  }
}

Gemini CLI setup guide

VS Code

Not tested by us yet

Open the install link, or run the command with the code CLI.
Or merge the JSON into .vscode/mcp.json in your project.
Run MCP: List Servers, start hugging-bay, and use Agent mode in Copilot Chat.

Add to VS Code

Add with the code CLI

code --add-mcp '{"name":"hugging-bay","type":"http","url":"https://huggingbay.xyz/api/mcp?profile=core"}'

.vscode/mcp.json

{
  "servers": {
    "hugging-bay": {
      "type": "http",
      "url": "https://huggingbay.xyz/api/mcp?profile=core"
    }
  }
}

VS Code setup guide

Windsurf

Not tested by us yet

Merge the JSON into ~/.codeium/windsurf/mcp_config.json.
Refresh MCP servers in Cascade settings, or restart Windsurf.

mcp_config.json

{
  "mcpServers": {
    "hugging-bay": {
      "serverUrl": "https://huggingbay.xyz/api/mcp?profile=core"
    }
  }
}

Windsurf setup guide

Hermes Agent

Connected in our test on 2026-11-05

Add the server to ~/.hermes/config.yaml.
Add the skill so Hermes knows when to use it.
Run hermes mcp test hugging-bay, then restart Hermes. If it says HTTP is not available, reinstall with pip install 'hermes-agent[mcp]'.

~/.hermes/config.yaml

mcp_servers:
  hugging-bay:
    url: "https://huggingbay.xyz/api/mcp?profile=core"

Add the skill (macOS and Linux)

d="$HOME/.hermes/skills/hugging-bay-models"; mkdir -p "$d/scripts" && curl -fsS https://huggingbay.xyz/skills/hugging-bay-models/SKILL.md -o "$d/SKILL.md" && curl -fsS https://huggingbay.xyz/skills/hugging-bay-models/scripts/hb.py -o "$d/scripts/hb.py"

Hermes Agent setup guide

OpenClaw

Connected in our test on 2026-11-05

Paste the command. OpenClaw connects once before it saves.
Run openclaw mcp probe to list the tools.

Add the server

openclaw mcp add hugging-bay --url 'https://huggingbay.xyz/api/mcp?profile=core' --transport streamable-http

OpenClaw setup guide

LangChain

Connected in our test on 2026-11-05

Install the adapter, then run the example. It prints the tool names.

Install

pip install langchain-mcp-adapters

Python

import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient

async def main():
    client = MultiServerMCPClient({
        "hugging-bay": {"transport": "streamable_http", "url": "https://huggingbay.xyz/api/mcp?profile=core"},
    })
    tools = await client.get_tools()
    print([tool.name for tool in tools])
    # Give the tools to your agent, for example create_agent(model, tools).

asyncio.run(main())

LangChain setup guide

LlamaIndex

Connected in our test on 2026-11-05

Install the MCP tools package, then run the example. It prints the tool names.

Install

pip install llama-index-tools-mcp

Python

import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec

async def main():
    spec = McpToolSpec(client=BasicMCPClient("https://huggingbay.xyz/api/mcp?profile=core"))
    tools = await spec.to_tool_list_async()
    print([tool.metadata.name for tool in tools])
    # Give the tools to your agent, for example FunctionAgent(tools=tools, llm=llm).

asyncio.run(main())

LlamaIndex setup guide

OpenAI Agents SDK

Connected in our test on 2026-11-05

Install the SDK, then run the example. It prints the tool names; uncomment the last line to ask a question.

Install

pip install openai-agents

Python

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp

async def main():
    async with MCPServerStreamableHttp(name="hugging-bay", params={"url": "https://huggingbay.xyz/api/mcp?profile=core"}, cache_tools_list=True) as hugging_bay:
        print([tool.name for tool in await hugging_bay.list_tools()])
        agent = Agent(name="Assistant", instructions="Use Hugging Bay to find open models.", mcp_servers=[hugging_bay])
        # result = await Runner.run(agent, "Find a chat model that fits 16 GB of RAM.")

asyncio.run(main())

OpenAI Agents SDK setup guide

Then ask your agent: find a chat model that fits my 16 GB laptop.

More agent docs

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