Verbalized Sampling MCP Server

A Model Context Protocol (MCP) server that provides Verbalized Sampling (VS) prompt templates and response processing utilities to mitigate mode collapse in LLM outputs.

Overview

Verbalized Sampling is a training-free prompting strategy that improves LLM diversity by 2-3x. It works by asking the model to generate multiple responses with their probabilities, then sampling from the tails of the distribution to encourage creative, less common outputs.

This MCP server provides three core tools that work together to implement the VS methodology:

1. vs_create_prompt - Generate optimized VS prompts for any task
2. vs_process_response - Parse LLM responses and select diverse outputs
3. vs_recommend_params - Get model-specific VS parameter recommendations

Features

Core VS Tools

• Prompt Generation: Creates research-backed VS prompts optimized for different models
• Response Processing: Parses XML-formatted responses and implements tail sampling
• Model Optimization: Provides parameter recommendations for 20+ current LLM models

Model Support

Supports the latest models from all major providers:

• Anthropic: Claude Sonnet 4.5, Haiku 4.5, Opus 4.1
• OpenAI: GPT-5.1, GPT-5 mini/nano/pro, GPT-4.1 series, o4-mini
• Google: Gemini 2.5 Pro/Flash, Gemini 1.5 Pro
• Meta/Open Source: Llama 3.3, DeepSeek R1, Qwen3

Installation

Option 1: Install from npm (Recommended)

npm install -g verbalized-sampling-mcp

Option 2: Install from Source

# Clone the repository
git clone https://github.com/johnferguson/verbalized-sampling-mcp.git
cd verbalized-sampling-mcp

# Install dependencies
npm install

# Configure environment variables (optional)
cp .env.example .env.development
# Edit .env.development with your Sentry DSN and other settings

# Build the project
npm run build

# Start the server
npm start

Sentry Monitoring

This server includes comprehensive Sentry monitoring for production observability:

Features

• Performance Monitoring: 100% trace sampling for detailed performance insights
• Error Tracking: MCP-specific error categorization and context
• Custom Metrics: VS tool execution times, success rates, confidence scores
• Health Monitoring: Server uptime, memory usage, connection tracking

Configuration

The server automatically detects environment and configures monitoring accordingly:

• Development: Full tracing with local error handling
• Production: Optimized performance with comprehensive error tracking

Environment Variables

# Required
SENTRY_DSN=https://your-dsn@sentry.io/project-id
SENTRY_ENVIRONMENT=development|production

# MCP-specific tags (automatically added to all events)
MCP_SERVER_NAME=verbalized-sampling-mcp
MCP_TRANSPORT_TYPE=stdio
MCP_TOOL_COUNT=4
MCP_CLIENT_INFO=vscode-extension@1.0.0

Monitoring Dashboard

View real-time metrics and errors at: Sentry Dashboard

For detailed monitoring setup and procedures, see OBSERVABILITY.md.

Usage

Quick Start

# Install and start
npm install -g verbalized-sampling-mcp
verbalized-sampling-mcp

# In another terminal, test with MCP Inspector
npx @modelcontextprotocol/inspector node dist/index.js

Basic Workflow

1. Generate VS Prompt: Use vs_create_prompt to get an optimized prompt
2. Send to LLM: Give the prompt to your LLM (via any interface)
3. Process Response: Use vs_process_response to parse and select the best diverse output

Examples

Example 1: Creative Writing with Claude

// Generate VS prompt for creative writing
const promptResult = await mcp.callTool("vs_create_prompt", {
  topic: "Write a short story about a robot learning to paint",
  method: "creative_writing", // Optimized for creative tasks
  model_name: "claude-sonnet-4-5"
});

// Send to Claude and get response
const claudeResponse = await callClaude(promptResult.content[0].text);

// Process for diverse selection
const storyResult = await mcp.callTool("vs_process_response", {
  llm_output: claudeResponse,
  tau: 0.08 // Model-specific threshold
});

console.log(storyResult.content[0].text); // Selected diverse story

Example 2: Technical Documentation with GPT-5

// Get model-specific parameters first
const params = await mcp.callTool("vs_recommend_params", {
  model_name: "gpt-5"
});
// Returns: {"k": 10, "tau": 0.05, "temperature": 1.1}

// Generate technical explanation prompt
const promptResult = await mcp.callTool("vs_create_prompt", {
  topic: "Explain quantum computing in simple terms",
  method: "cot", // Chain-of-thought for complex topics
  model_name: "gpt-5"
});

// Process GPT's XML response
const result = await mcp.callTool("vs_process_response", {
  llm_output: gptResponse,
  tau: params.tau // Use research-optimized threshold
});

Example 3: Dialogue Generation

// Generate diverse dialogue responses
const promptResult = await mcp.callTool("vs_create_prompt", {
  topic: "Write a conversation between a human and AI about climate change",
  method: "dialogue", // Specialized for conversation
  model_name: "gemini-2.5-pro"
});

// Get multiple dialogue options
const dialogueResult = await mcp.callTool("vs_process_response", {
  llm_output: geminiResponse,
  tau: 0.12 // Gemini-specific threshold
});

Example 4: Batch Processing

// Process multiple responses efficiently
const responses = [
  "<response><text>Option A</text><probability>0.15</probability></response>",
  "<response><text>Option B</text><probability>0.07</probability></response>",
  "<response><text>Option C</text><probability>0.03</probability></response>"
];

for (const response of responses) {
  const result = await mcp.callTool("vs_process_response", {
    llm_output: response,
    tau: 0.10 // Standard threshold
  });
  console.log(`Selected: ${result.content[0].text}`);
}

MCP Integration

Claude Desktop (Recommended)

1. Install from npm:

npm install -g verbalized-sampling-mcp

2. Add to Claude Desktop:
   • Open Claude Desktop → Settings → Developer → Edit MCP Servers
   • Add new server:

     {
       "name": "verbalized-sampling-mcp",
       "command": "verbalized-sampling-mcp",
       "args": []
     }
     

   • Restart Claude Desktop

Other MCP Clients

{
  "mcpServers": {
    "verbalized-sampling": {
      "command": "node",
      "args": ["/path/to/verbalized-sampling-mcp/dist/index.js"]
    }
  }
}

Environment Variables (Optional)

# Sentry monitoring (recommended for production)
export SENTRY_DSN="your-dsn@sentry.io/project-id"
export SENTRY_ENVIRONMENT="production"

# Or create .env file
echo "SENTRY_DSN=your-dsn@sentry.io/project-id" > .env
echo "SENTRY_ENVIRONMENT=production" >> .env

Available Tools

vs_create_prompt

Generates a Verbalized Sampling prompt optimized for a specific model and task.

Parameters:

• topic (string, required): The user's query or task
• method (string, optional): VS strategy - "standard", "cot", or "multi-turn"
• model_name (string, optional): Target model name for parameter optimization

Returns: A complete VS prompt string ready to send to an LLM.

vs_process_response

Parses an LLM's XML response and selects the most diverse option using tail sampling.

Parameters:

• llm_output (string, required): Raw text output from LLM containing <response> tags
• tau (number, optional): Probability threshold for tail sampling (default: 0.10)

Returns: The selected diverse response with metadata.

vs_recommend_params

Gets recommended VS parameters for a specific model.

Parameters:

• model_name (string, required): The model name to look up

Returns: JSON object with k (sample count), tau (threshold), and temperature values.

MCP Server Details

Server Configuration

The server runs on stdio transport and provides these MCP tools:

Tool	Description	Parameters
vs_create_prompt	Generate optimized VS prompts	topic (required), method, model_name
vs_process_response	Parse XML responses and select diverse output	llm_output (required), tau
vs_recommend_params	Get model-specific VS parameters	model_name (required)

VS Methods Available

Method	Description	Best For
standard	Basic VS prompting	General use
cot	Chain-of-thought reasoning	Complex tasks
multi-turn	Progressive diversity building	Conversations
research_standard	Official research format	Research compliance
creative_writing	Optimized for creativity	Stories, poems
dialogue	Varied tone/style	Conversations

Model Support

Supports 20+ models with optimized parameters:

Anthropic: Claude Sonnet 4.5, Haiku 4.5, Opus 4.1 OpenAI: GPT-5, GPT-5 mini/nano/pro, GPT-4.1 series, o4-mini
Google: Gemini 2.5 Pro/Flash, Gemini 1.5 Pro Meta/Open Source: Llama 3.3, DeepSeek R1, Qwen3

Development

# Development mode
npm run dev

# Run tests
npm test

# Test Sentry integration
npm run sentry:test

# Lint and fix code
npm run lint:fix

# Format code
npm run format

# Type checking
npm run typecheck

Production Deployment

Environment Setup

# Production environment
export NODE_ENV=production
export SENTRY_DSN="your-dsn@sentry.io/project-id"
export SENTRY_ENVIRONMENT=production

# Start with monitoring
npm start

Docker Deployment

FROM node:18-alpine
WORKDIR /app
COPY package*.json ./
RUN npm ci --only=production
COPY dist ./dist/
EXPOSE 3000
CMD ["npm", "start"]

Monitoring

The server includes comprehensive Sentry monitoring:

• Performance Metrics: 100% trace sampling
• Error Tracking: MCP-specific error categorization
• Custom Metrics: VS tool execution times, success rates
• Health Monitoring: Server uptime, memory usage, connections

View metrics at: Sentry Dashboard

Testing Sentry Integration

# Test error reporting
npm run sentry:test

# Start server with monitoring
npm run start

# Use MCP Inspector to test tools and verify metrics
npx @modelcontextprotocol/inspector node dist/index.js

All tool executions, errors, and performance metrics are automatically sent to Sentry with MCP-specific context.

Architecture

src/
├── tools/
│   ├── vs-tools.ts        # Main MCP tool implementations
│   ├── prompts.ts         # VS prompt templates and formatting
│   ├── sampler.ts         # Response parsing and selection logic
│   └── constants.ts       # Model-specific parameter mappings
└── index.ts               # MCP server setup

Scientific Foundation

This implementation is based on the research paper "Verbalized Sampling: How to Mitigate Mode Collapse and Unlock LLM Diversity" by Zhang et al. (2025), which demonstrates that VS increases diversity by 1.6-2.1x while maintaining quality.

The methodology works by:

1. Prompting for Probabilities: Asking LLMs to verbalize probability estimates for their own outputs
2. Tail Sampling: Selecting responses with low probabilities to encourage diversity
3. XML Structure: Using structured output format for reliable parsing

Contributing

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Ensure all tests pass
5. Submit a pull request

License

MIT License - see LICENSE file for details.

Related

• Verbalized Sampling Research
• Model Context Protocol
• Research Paper