Skip to main content
Glama
README.md
# AI Consultant MCP Server

A Model Context Protocol (MCP) server that enables AI agents to consult with multiple AI models through OpenRouter. Features intelligent model auto-selection, conversation history, caching, and robust error handling.

## What is this?

This MCP server allows your AI assistant (like Claude Desktop) to consult with various AI models (GPT, Gemini, Grok, etc.) through a single interface. It automatically selects the best model for your task or lets you choose a specific one.

## Quick Start

### Installation from npm

```bash
npm install -g ai-consultant-mcp
```

### Prerequisites

You'll need an OpenRouter API key. Get one at [OpenRouter](https://openrouter.ai/).

## Configuration

### Option 1: Using npm package (Recommended)

Edit your MCP client configuration file:

**For Claude Desktop:**

- macOS: `~/Library/Application Support/Claude/claude_desktop_config.json`
- Windows: `%APPDATA%\Claude\claude_desktop_config.json`

```json
{
  "mcpServers": {
    "ai-consultant": {
      "command": "npx",
      "args": ["-y", "ai-consultant-mcp"],
      "env": {
        "OPENROUTER_API_KEY": "your-openrouter-api-key"
      }
    }
  }
}
```

**For other MCP clients:**

Configure according to your client's documentation, using `npx -y ai-consultant-mcp` as the command.

### Option 2: Running locally (Development)

1. Clone the repository:

```bash
git clone https://github.com/filipkrayem/ai-consultant-mcp.git
cd ai-consultant-mcp
```

2. Install dependencies:

```bash
npm install
```

3. Build the project:

```bash
npm run build
```

4. Configure your MCP client:

```json
{
  "mcpServers": {
    "ai-consultant": {
      "command": "node",
      "args": ["/absolute/path/to/ai-consultant-mcp/dist/index.js"],
      "env": {
        "OPENROUTER_API_KEY": "your-openrouter-api-key"
      }
    }
  }
}
```

### Environment Variables

- `OPENROUTER_API_KEY` (required): Your OpenRouter API key
- `VERBOSE_LOGGING` (optional): Set to `true` or `1` to enable detailed logging. Default: `false`

## Available Models

- **gemini-2.5-pro**: Google's Gemini 2.5 Pro - general purpose tasks and quick questions
- **gpt-5-codex**: OpenAI's GPT-5 Codex - coding tasks, debugging, and refactoring
- **grok-code-fast-1**: xAI's Grok Code Fast 1 - code review, complex reasoning, and analysis

## Features

- 🤖 **Multiple AI models** - Access GPT, Gemini, Grok, and more through one interface
- 🎯 **Smart model selection** - Automatically picks the best model for your task
- 💬 **Conversation history** - Maintain context across multiple questions
- ⚡ **Response caching** - Reduces API calls and costs
- 🔄 **Automatic retries** - Handles transient failures gracefully
- 🛡️ **Circuit breaker** - Prevents cascading failures
- 📊 **Token tracking** - Monitor usage for each consultation

## Usage

Once configured, your AI assistant can use these tools:

- **`consult_ai`** - Ask questions to AI models (auto-selects or specify a model)
- **`list_models`** - See all available models and their capabilities

Simply ask your AI assistant to consult with AI models. For example:

- "Consult this change with Grok and Codex"
- "Have Grok review your code first"

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

MIT

## Links

- [GitHub Repository](https://github.com/filipkrayem/ai-consultant-mcp)
- [npm Package](https://www.npmjs.com/package/ai-consultant-mcp)
- [OpenRouter](https://openrouter.ai/)
- [Model Context Protocol](https://modelcontextprotocol.io/)

TDQS

B3.4/5.0

Scored across 2 tools

Disambiguation5/5

The two tools have completely distinct purposes: consult_ai is for executing AI consultations, while list_models is for retrieving model information. There is no overlap in functionality, making it impossible for an agent to confuse them.

Naming Consistency5/5

Both tools follow a consistent verb_noun pattern (consult_ai, list_models) with clear, descriptive names. The naming convention is uniform and predictable across the set.

Tool Count2/5

With only two tools, the server feels under-scoped for an 'AI Consultant' domain. While the tools cover consultation and model listing, there are likely missing operations like managing consultation history, configuring model parameters, or handling feedback, making the set feel incomplete for the stated purpose.

Completeness2/5

For an AI consultant server, the tool surface is severely incomplete. It lacks essential operations such as saving or retrieving past consultations, adjusting consultation settings, or providing feedback on model performance. The current tools only cover the most basic consultation flow, leaving significant gaps that will hinder agent workflows.

Maintenance

ActivityInactive
ResponsivenessNo issues