Multi Model Advisor: AI Synergy & Data-Driven Decisions

Multi Model Advisor: Seamlessly blends top AI models to maximize performance—your expert navigator for smarter, data-driven decisions.

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About Multi Model Advisor

What is Multi Model Advisor: AI Synergy & Data-Driven Decisions?

Multi Model Advisor is a Model Context Protocol (MCP) server designed to leverage the collective intelligence of multiple Ollama-based AI models. By aggregating responses from diverse models such as Gemma3, Llama3.2, and Deepseek-r1, it creates a collaborative decision-making framework. This system enables users to synthesize perspectives from distinct "personas" assigned to each model, enhancing decision quality through algorithmic consensus. The architecture emphasizes synergistic AI interactions while maintaining compatibility with platforms like Claude for Desktop.

Key Features of Multi Model Advisor: AI Synergy & Data-Driven Decisions

Persona-driven model interaction: Assign distinct roles to each model to encourage complementary perspectives
Programmatic response aggregation: Automatically collate and prioritize outputs from 3+ concurrent model queries
Customizable workflows: Override default behaviors through YAML-based configuration profiles
Seamless API integration: Expose model ensembles as RESTful endpoints for enterprise systems
Real-time logging: Track model confidence scores and decision rationale through standardized JSON outputs

Multi Model Advisor Features

How to Use Multi Model Advisor: AI Synergy & Data-Driven Decisions

Implementation follows a three-phase approach:

Configuration Phase: Define model ensembles and role assignments in the .env configuration. Example persona configuration:

MODEL_ENSEMBLE="Gemma3:creative,Llama3.2:analytical,Deepseek-r1:critical"

Execution Phase: Submit query payloads to the /api/ensemble endpoint specifying decision criteria weightings. Example request body:

{
  "query": "Optimize marketing strategy for Q4",
  "priority": ["innovation", "ROI", "risk_aversion"],
  "confidence_threshold": 0.85
}

Analysis Phase: Review the aggregated response containing model votes, confidence metrics, and recommended action pathways.

Use Cases of Multi Model Advisor: AI Synergy & Data-Driven Decisions

Optimal applications include:

Strategic decision-making in ambiguous conditions where conflicting priorities exist
Risk assessment requiring simultaneous evaluation of technical, financial, and ethical dimensions
Innovation brainstorming sessions needing structured creativity and feasibility analysis
Regulatory compliance scenarios where multiple legal interpretations must be considered

Multi Model Advisor FAQ

FAQ from Multi Model Advisor: AI Synergy & Data-Driven Decisions

Why use ensemble models instead of a single advanced model?: Ensemble methods reduce bias by exposing decision-making to varied architectural strengths. For instance, transformer-based models excel at pattern recognition while knowledge-graph models handle ontological reasoning better.
How are conflicting model recommendations resolved?: A three-tier resolution process applies: 1) Confidence-weighted voting, 2) Priority heuristic scoring, 3) Human-in-the-loop override for critical decisions above defined risk thresholds.
Can custom models be integrated?: Yes, through the Ollama container API. Developers can register self-hosted models using the ollama serve command and update the MODEL_REGISTRY variable in configuration.
What security measures are implemented?: Role-based access control (RBAC) for API endpoints, encrypted model communication channels, and audit trails for all decision-making sessions.

Content

Multi-Model Advisor

A Model Context Protocol (MCP) server that queries multiple Ollama models and combines their responses, providing diverse AI perspectives on a single question. This creates a "council of advisors" approach where Claude can synthesize multiple viewpoints alongside its own to provide more comprehensive answers.

Features

Query multiple Ollama models with a single question
Assign different roles/personas to each model
View all available Ollama models on your system
Customize system prompts for each model
Configure via environment variables
Integrate seamlessly with Claude for Desktop

Prerequisites

Node.js 16.x or higher
Ollama installed and running (see Ollama installation)
Claude for Desktop (for the complete advisory experience)

Installation

Clone this repository:

git clone https://github.com/yourusername/multi-model-advisor.git

cd multi-model-advisor

Install dependencies:

npm install
Build the project:

npm run build
Install required Ollama models:

ollama pull gemma3:1b

ollama pull llama3.2:1b
ollama pull deepseek-r1:1.5b

Configuration

Create a .env file in the project root with your desired configuration:

# Server configuration
SERVER_NAME=multi-model-advisor
SERVER_VERSION=1.0.0
DEBUG=true

# Ollama configuration
OLLAMA_API_URL=http://localhost:11434
DEFAULT_MODELS=gemma3:1b,llama3.2:1b,deepseek-r1:1.5b

# System prompts for each model
GEMMA_SYSTEM_PROMPT=You are a supportive and empathetic AI assistant focused on human well-being. Provide considerate and balanced advice.
LLAMA_SYSTEM_PROMPT=You are a logical and analytical AI assistant. Think step-by-step and explain your reasoning clearly.
DEEPSEEK_SYSTEM_PROMPT=You are a creative and innovative AI assistant. Think outside the box and offer novel perspectives.

Connect to Claude for Desktop

Locate your Claude for Desktop configuration file:

* MacOS: `~/Library/Application Support/Claude/claude_desktop_config.json`
* Windows: `%APPDATA%\Claude\claude_desktop_config.json`

Edit the file to add the Multi-Model Advisor MCP server:

{
  "mcpServers": {
    "multi-model-advisor": {
      "command": "node",
      "args": ["/absolute/path/to/multi-model-advisor/build/index.js"]
    }
  }
}

Replace /absolute/path/to/ with the actual path to your project directory
Restart Claude for Desktop

Usage

Once connected to Claude for Desktop, you can use the Multi-Model Advisor in several ways:

List Available Models

You can see all available models on your system:

Show me which Ollama models are available on my system

This will display all installed Ollama models and indicate which ones are configured as defaults.

Basic Usage

Simply ask Claude to use the multi-model advisor:

what are the most important skills for success in today's job market, 
you can use gemma3:1b, llama3.2:1b, deepseek-r1:1.5b to help you

Claude will query all default models and provide a synthesized response based on their different perspectives.

example

How It Works

The MCP server exposes two tools:

* `list-available-models`: Shows all Ollama models on your system
* `query-models`: Queries multiple models with a question

When you ask Claude a question referring to the multi-model advisor:

* Claude decides to use the `query-models` tool
* The server sends your question to multiple Ollama models
* Each model responds with its perspective
* Claude receives all responses and synthesizes a comprehensive answer

Each model can have a different "persona" or role assigned, encouraging diverse perspectives.

Troubleshooting

Ollama Connection Issues

If the server can't connect to Ollama:

Ensure Ollama is running (ollama serve)
Check that the OLLAMA_API_URL is correct in your .env file
Try accessing http://localhost:11434 in your browser to verify Ollama is responding

Model Not Found

If a model is reported as unavailable:

Check that you've pulled the model using ollama pull <model-name>
Verify the exact model name using ollama list
Use the list-available-models tool to see all available models

Claude Not Showing MCP Tools

If the tools don't appear in Claude:

Ensure you've restarted Claude after updating the configuration
Check the absolute path in claude_desktop_config.json is correct
Look at Claude's logs for error messages

License

MIT License

For more details, please see the LICENSE file in this project repository

Contributing

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