analytical-mcp

quanticsoul4772/analytical-mcp

3.3

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The Analytical MCP Server is a specialized Model Context Protocol server offering advanced analytical, research, and natural language processing capabilities.

Analytical MCP Server

Model Context Protocol server providing statistical analysis, decision support, logical reasoning, and research verification tools for Claude.

Setup

Prerequisites

  • Node.js >= 20.0.0
  • EXA_API_KEY environment variable (for research features)

Installation

Option 1: Direct Installation
npm install
npm run build
Option 2: Docker
# Build the Docker image
docker build -t analytical-mcp .

# Run with environment variables
docker run -d \
  --name analytical-mcp \
  -e EXA_API_KEY=your_api_key_here \
  -v $(pwd)/cache:/app/cache \
  analytical-mcp

# Or use docker-compose
cp .env.example .env
# Edit .env with your API key
docker-compose up -d

Configuration

Direct Installation Configuration
  1. Copy .env.example to .env
  2. Add your EXA_API_KEY to .env
  3. Add to Claude Desktop configuration:
{
  "mcpServers": {
    "analytical": {
      "command": "node",
      "args": ["/path/to/analytical-mcp/build/index.js"],
      "env": {
        "EXA_API_KEY": "your-exa-api-key-here"
      }
    }
  }
}
Docker Configuration
  1. Copy .env.example to .env
  2. Add your EXA_API_KEY to .env
  3. Add to Claude Desktop configuration:
{
  "mcpServers": {
    "analytical": {
      "command": "docker",
      "args": [
        "run", "--rm", "-i",
        "--env-file", ".env",
        "-v", "$(pwd)/cache:/app/cache",
        "analytical-mcp"
      ]
    }
  }
}

Available Tools

Statistical Analysis

  • analytical:analyze_dataset - Statistical analysis of datasets
  • analytical:advanced_regression_analysis - Linear, polynomial, and logistic regression
  • analytical:hypothesis_testing - Statistical hypothesis testing (t-tests, chi-square, ANOVA)
  • analytical:data_visualization_generator - Generate data visualization specifications

Decision Analysis

  • analytical:decision_analysis - Multi-criteria decision analysis with weighted scoring

Logical Reasoning

  • analytical:logical_argument_analyzer - Analyze argument structure and validity
  • analytical:logical_fallacy_detector - Detect logical fallacies in text
  • analytical:perspective_shifter - Generate alternative perspectives on problems

Research Verification

  • analytical:verify_research - Cross-verify research claims from multiple sources

Observability & Metrics

The Analytical MCP Server includes built-in observability features for monitoring circuit breakers and cache performance.

Metrics Endpoint

When enabled, the server exposes metrics via HTTP on port 9090 (configurable):

  • http://localhost:9090/metrics - Prometheus-style metrics
  • http://localhost:9090/metrics?format=json - JSON format metrics
  • http://localhost:9090/health - Health check endpoint
  • http://localhost:9090/ - Metrics server status page

Available Metrics

Circuit Breaker Metrics
  • analytical_mcp_circuit_breaker_state - Current state (0=CLOSED, 1=HALF_OPEN, 2=OPEN)
  • analytical_mcp_circuit_breaker_total_calls_total - Total calls through circuit breaker
  • analytical_mcp_circuit_breaker_rejected_calls_total - Rejected calls by circuit breaker
  • analytical_mcp_circuit_breaker_failure_count - Current failure count
  • analytical_mcp_circuit_breaker_success_count - Current success count
Cache Metrics
  • analytical_mcp_cache_hits_total - Cache hits by namespace
  • analytical_mcp_cache_misses_total - Cache misses by namespace
  • analytical_mcp_cache_puts_total - Cache puts by namespace
  • analytical_mcp_cache_evictions_total - Cache evictions by namespace
  • analytical_mcp_cache_size - Current cache size by namespace
System Metrics
  • analytical_mcp_uptime_seconds - Server uptime in seconds
  • analytical_mcp_memory_usage_bytes - Memory usage (RSS, heap, external)
  • analytical_mcp_cpu_usage_microseconds - CPU time usage (user, system)

Configuration

Enable metrics by setting environment variables:

METRICS_ENABLED=true        # Enable metrics server (default: true)
METRICS_PORT=9090          # Metrics server port (default: 9090)
METRICS_HOST=127.0.0.1     # Metrics server host (default: 127.0.0.1, use 0.0.0.0 to bind to all interfaces)

Usage Examples

# Get Prometheus metrics
curl http://localhost:9090/metrics

# Get JSON metrics
curl http://localhost:9090/metrics?format=json

# Health check
curl http://localhost:9090/health

Usage Examples

Dataset Analysis

{
  "data": [23, 45, 67, 12, 89, 34, 56, 78],
  "analysisType": "stats"
}

Decision Analysis

{
  "options": ["Option A", "Option B", "Option C"],
  "criteria": ["Cost", "Quality", "Speed"],
  "weights": [0.4, 0.4, 0.2]
}

Logical Analysis

{
  "argument": "All birds can fly. Penguins are birds. Therefore, penguins can fly.",
  "analysisDepth": "comprehensive"
}

Development

Testing

# Run all tests
./tools/test-runner.sh

# Run specific test suite
./tools/test-runner.sh integration

# Available test suites: api-keys, server, integration, research, data-pipeline

Scripts

  • npm run build - Build TypeScript to JavaScript
  • npm run watch - Watch for changes and rebuild
  • npm run test - Run Jest tests
  • npm run inspector - Start MCP inspector for debugging

Project Structure

analytical-mcp/
โ”œโ”€โ”€ src/
โ”‚   โ”œโ”€โ”€ tools/           # MCP tool implementations
โ”‚   โ”œโ”€โ”€ utils/           # Utility functions
โ”‚   โ””โ”€โ”€ index.ts         # Main server entry point
โ”œโ”€โ”€ docs/                # Documentation
โ”œโ”€โ”€ tools/               # Development and testing scripts
โ””โ”€โ”€ examples/            # Usage examples

Tool Categories

Statistical Analysis

  • Descriptive statistics: mean, median, standard deviation, quartiles
  • Correlation analysis
  • Regression analysis: linear, polynomial, logistic
  • Hypothesis testing: t-tests, chi-square, ANOVA

Decision Support

  • Multi-criteria decision analysis
  • Weighted scoring
  • Trade-off analysis
  • Risk assessment

Logical Reasoning

  • Argument structure analysis
  • Fallacy detection
  • Perspective generation
  • Critical analysis

Research Integration

  • Multi-source verification
  • Fact extraction
  • Consistency checking
  • Confidence scoring

Security and Privacy

  • All processing occurs locally
  • Research features use Exa API (optional, requires API key)
  • No permanent data storage
  • Optional file-based caching stored locally only
  • API keys managed via environment variables

License

MIT License. See LICENSE file for details.

Contributing

  1. Fork the repository
  2. Create feature branch: git checkout -b feature/your-feature
  3. Make changes and commit: git commit -m 'Add feature description'
  4. Push to branch: git push origin feature/your-feature
  5. Open pull request

See for detailed development guidelines, code standards, and testing requirements.

Troubleshooting

Common Issues

JSON parsing errors: All logging must go to stderr, not stdout. MCP protocol uses stdout for communication. Use the Logger class, not console.log.

Tools not appearing: Verify server configuration in Claude Desktop settings and restart Claude Desktop application.

Research features disabled: Set EXA_API_KEY in your environment or .env file.

Server not starting: Check Node.js version is 20 or higher and all dependencies are installed with npm install.

See for detailed troubleshooting guidance.

Debug Mode

Start the server with the MCP inspector:

npm run inspector

Links