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adorbis-mcp

adorbis-mcp

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github.com/adorbistechAI & Agents1Updated 18w ago

About adorbis-mcp

adorbis-mcp is an MCP server for Adorbis AI, giving developers using MCP-compatible tools access to chat, coding, and analysis. It automatically routes requests across available models and also supports multi-turn conversations.

Key features

  1. 01Automatic routing to available models
  2. 02General chat and multi-turn conversations
  3. 03Code generation, debugging, and review
  4. 04Math and multi-step reasoning
  5. 05Writing and balance checks

Use cases

  1. 01Generate or review code in a coding workflow
  2. 02Handle continuing requests with multi-turn chat
  3. 03Check available departments and account balance

MCP support

The MCP server exposes Adorbis AI chat, coding, reasoning, writing, balance, and model-list tools to MCP clients. It calls the Adorbis API with one ADORBIS_API_KEY and routes requests automatically.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
"mcpServers": {
"adorbistech-adorbis": {
"command": "npx",
"args": [
"-y",
"@adorbistech/mcp-server"
],
"env": {
"ADORBIS_API_KEY": "<ADORBIS_API_KEY>"
}
}
}
}
Run commandnpx -y @adorbistech/mcp-server
Environment variables to set
ADORBIS_API_KEYSecretAdorbis API key from ai.adorbistech.com

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
npm@adorbistech/mcp-server1.0.2

Tools

7

Saved tool list · updated 2w ago

ParameterTypeRequiredDescription
messagestringYesThe message or question to send to the AI
departmentenumNoAI department to use: 'adorbis/quick' (fast responses), 'adorbis/coder' (code), 'adorbis/auto' (smart routing), 'adorbis/write' (writing), 'adorbis/reason' (analysis)adorbis/coder | adorbis/quick | adorbis/auto | adorbis/write | adorbis/reason
max_tokensintegerNoMaximum tokens in the response
system_promptstringNoOptional system prompt to set context for the AI

Details

From the project README

@adorbistech/mcp-server

Adorbis AI MCP Server

One API key. 40+ models. Auto-routed. Use Adorbis AI from Claude Code, Cursor, Cline, and any MCP-compatible tool.

npm version license node

Get a free API key · Documentation · Dashboard


Install

Option 1 — Clone and run locally (recommended, works on all platforms)
git clone https://github.com/adorbistech/adorbis-mcp
cd adorbis-mcp
npm install
npm run build

Then point your tool to node /path/to/adorbis-mcp/dist/index.js.


Claude Code

Add to ~/.claude.json:

{
  "mcpServers": {
    "adorbis": {
      "command": "node",
      "args": ["/path/to/adorbis-mcp/dist/index.js"],
      "env": {
        "ADORBIS_API_KEY": "your_key_here"
      }
    }
  }
}

Windows path example:

"args": ["D:\\Ai-Adorbis\\MCP\\dist\\index.js"]

Mac/Linux path example:

"args": ["/home/user/adorbis-mcp/dist/index.js"]

Cursor

Add to ~/.cursor/mcp.json:

{
  "mcpServers": {
    "adorbis": {
      "command": "node",
      "args": ["/path/to/adorbis-mcp/dist/index.js"],
      "env": {
        "ADORBIS_API_KEY": "your_key_here"
      }
    }
  }
}

Cline (VS Code)

Add to Cline MCP settings:

{
  "adorbis": {
    "command": "node",
    "args": ["/path/to/adorbis-mcp/dist/index.js"],
    "env": {
      "ADORBIS_API_KEY": "your_key_here"
    }
  }
}

Option 2 — npx (Mac/Linux only)
# Mac/Linux
ADORBIS_API_KEY=your_key npx @adorbistech/mcp-server

Note: npx install is not recommended on Windows due to path resolution issues. Use the clone method above.


Auto-configure with CLI
npx adorbis-init --mcps

Automatically writes the correct MCP config for every tool you have configured.


Available Tools

Once connected, your AI assistant has access to 7 tools:

ToolDescription
adorbis_chatGeneral AI chat — auto-routes to best model
adorbis_codeCode generation, debugging, review
adorbis_reasonMath, logic, multi-step analysis
adorbis_writeDocumentation, articles, emails
adorbis_balanceCheck connection and credit status
adorbis_modelsList all available departments
adorbis_multi_turnMulti-turn conversation with history
Usage examples
Check my Adorbis balance
Use adorbis_code to write a TypeScript debounce function
What Adorbis departments are available?
Use adorbis_reason to analyze the time complexity of merge sort

Departments

DepartmentBest forSpeed
adorbis/quickChat, Q&A, simple tasksFastest
adorbis/coderCode generation, debugging, reviewFast
adorbis/autoMixed workloads, smart routingVaries
adorbis/writeDocumentation, articles, emailsMedium
adorbis/reasonMath, logic, complex analysisThorough

How Adorbis AI works

Your tool (Claude Code / Cursor / Cline)
              │
              ▼ MCP call
  @adorbistech/mcp-server
              │
              ▼ HTTPS
   api.adorbistech.com  ←── your single API key
              │
      ┌───────┴────────┐
      │   Department   │
      │    routing     │
      └───────┬────────┘
              │
    ┌─────────┼──────────┐
    ▼         ▼          ▼
 Gemini   DeepSeek   Claude
 Flash      V3       Sonnet
              │
              ▼
         Best model
         for your task

Adorbis monitors model health, latency, and cost in real time. When you send a request, it routes to the best available model automatically. No manual model switching. No vendor lock-in.


Environment Variables

VariableRequiredDescription
ADORBIS_API_KEYYesYour Adorbis API key

Get a free key (1,000 credits/month, no credit card) at https://ai.adorbistech.com


Plans

PlanPriceCredits
Free$0/mo1,000 AC
Starter$10/mo15,000 AC
Pro$30/mo50,000 AC
Business$100/mo200,000 AC

1 AC = $0.001 of underlying compute. Upgrade →


Also available

  • VS Code Extension — ext install adorbis.adorbis-ai
  • CLI setup wizard — npx adorbis-init
  • Full docs — ai.adorbistech.com/docs

Requirements


Contributing

Issues and PRs welcome at github.com/adorbistech/adorbis-mcp


License

MIT © Adorbis Technologies LLC

Comments

Markdown supported

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.