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Analytical MCP Server

Analytical MCP Server

A Model Context Protocol (MCP) server that provides statistical analysis, decision-making, and logical reasoning tools.

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/ ├── src/ │ ├── tools/ # MCP tool implementations │ ├── utils/ # Utility functions │ └── index.ts # Main server entry point ├── docs/ # Documentation ├── tools/ # Development and testing scripts └── 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 systems
  • Trade-off analysis
  • Risk assessment

Logical Reasoning

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

Research Integration

  • Multi-source verification
  • Fact extraction
  • Consistency checking
  • Research validation

Security & Privacy

  • Processing is done locally
  • Research features use Exa API (optional)
  • No data is stored permanently
  • Configurable caching with local-only storage

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing

  1. Fork the repository
  2. Create your feature branch (git checkout -b feature/feature-name)
  3. Commit your changes (git commit -m 'Add feature description')
  4. Push to the branch (git push origin feature/feature-name)
  5. Open a Pull Request

For detailed contribution guidelines, see docs/DEVELOPMENT.md.

Troubleshooting

Common Issues

JSON parsing errors: Ensure all logging goes to stderr, not stdout. The MCP protocol uses stdout for communication.

Tools not appearing: Verify the server is properly configured in Claude Desktop and restart the application.

Research features disabled: Check that EXA_API_KEY is set in your environment configuration.

Console output issues: The project uses a Logger class for all output. Utility scripts in the tools/ directory integrate with the Logger system for consistent formatting.

Debug Mode

Start the server with the MCP inspector:

npm run inspector
-
security - not tested
A
license - permissive license
-
quality - not tested

remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

モデル コンテキスト プロトコル サーバーを通じて高度な分析、調査、自然言語処理機能を提供し、データセット分析、意思決定分析、エンティティ認識やファクト抽出などの強化された NLP 機能を可能にします。

  1. 主な特徴
    1. 分析ツール
    2. 高度なNLP機能
  2. インストール
    1. 前提条件
    2. 設定
  3. 使用法
    1. ランニングツール
    2. 高度なNLPデモ
  4. 発達
    1. 利用可能なスクリプト
    2. テストスクリプト
    3. 主要技術
  5. 高度なNLP実装
    1. 必要なAPIキー
      1. 貢献
        1. ライセンス

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