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kongen-mcp

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by kongen-labs
README.md
# kongen-mcp

MCP (Model Context Protocol) server for the [Kongen Labs](https://kongenlabs.life) Pattern Intelligence API.

Connects Claude Code, Cursor, Windsurf, and any MCP-compatible client to Kongen's cross-domain pattern intelligence — reasoning regime detection, structural transfer scoring, and intelligent model routing.

## Tools

| Tool | Description | Cost |
|------|-------------|------|
| `score_prompt` | Detect reasoning regime and optimal token budget for a prompt | 1 KT |
| `transfer_score` | Score a structural signature against cross-domain reference patterns | 50 KT |
| `check_usage` | Check your token balance and usage | 0 KT |
| `route_model` | Recommend Haiku/Sonnet/Opus based on prompt complexity | 1 KT |

## Installation

```bash
pip install kongenlabs-mcp
```

Or install from source:

```bash
git clone https://github.com/kongen-labs/kongen-mcp.git
cd kongen-mcp
pip install .
```

## Configuration

Get your API key at [kongenlabs.life](https://kongenlabs.life).

### Claude Code

Add with the CLI:

```bash
claude mcp add kongen -- kongen-mcp
```

Then set your API key when prompted, or add it manually to `.claude/mcp.json`:

```json
{
  "mcpServers": {
    "kongen": {
      "command": "kongen-mcp",
      "env": {
        "KONGEN_API_KEY": "kl_live_..."
      }
    }
  }
}
```

### Cursor

1. Open **Settings** > **MCP** > **Add Server**
2. Name: `kongen`
3. Command: `kongen-mcp`
4. Environment variables:
   - `KONGEN_API_KEY`: your API key

### Windsurf

Add to your MCP configuration:

```json
{
  "mcpServers": {
    "kongen": {
      "command": "kongen-mcp",
      "env": {
        "KONGEN_API_KEY": "kl_live_..."
      }
    }
  }
}
```

### Custom base URL

To point the server at a different API endpoint (for example a local proxy), set `KONGEN_API_BASE_URL`:

```json
{
  "mcpServers": {
    "kongen": {
      "command": "kongen-mcp",
      "env": {
        "KONGEN_API_KEY": "kl_test_...",
        "KONGEN_API_BASE_URL": "http://localhost:8000"
      }
    }
  }
}
```

## Usage examples

Once configured, the tools are available to your AI assistant. Examples of what you can ask:

**Prompt scoring:**
> "Score this prompt for complexity: Prove that the square root of 2 is irrational using proof by contradiction."

**Model routing:**
> "Which Claude model should I use for this task: Summarize this 3-line email."

**Transfer scoring:**
> "Score this structural signature against cross-domain patterns."

**Usage check:**
> "How many Kongen tokens do I have left?"

## Environment variables

| Variable | Required | Default | Description |
|----------|----------|---------|-------------|
| `KONGEN_API_KEY` | Yes | — | Your Kongen API key (`kl_live_...` or `kl_test_...`) |
| `KONGEN_API_BASE_URL` | No | `https://api.kongenlabs.life` | API base URL override |

## License

MIT

<!-- mcp-name: io.github.fisnik/kongen-mcp -->

TDQS

A4/5.0

Scored across 4 tools

Disambiguation5/5

Each tool has a clearly distinct purpose: checking usage, recommending models, scoring prompts, and scoring transfer vectors. No overlapping functionality.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case (check_usage, route_model, score_prompt, transfer_score), making them predictable.

Tool Count5/5

With 4 tools, the set is well-scoped for the server's purpose, covering core operations without being too thin or bloated.

Completeness4/5

The tools cover primary use cases (check usage, route/score prompts, transfer scoring). Minor gaps like account management or token budget adjustments are missing but not critical for the core workflow.

Maintenance

ActivityInactive
ResponsivenessNo issues